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Automated seizure detection systems and their effectiveness for each type of seizure

Open ArchivePublished:June 17, 2016DOI:https://doi.org/10.1016/j.seizure.2016.06.008

      Highlights

      • Patient specific algorithms are crucial for achieving accurate detection devices.
      • Multimodal detection systems are needed to meet the requirements of seizure detection.
      • Closed-loop systems are recommended because they provide active feedback.
      • The systems will improve as information from different patients accumulates.
      • A proposal of seizure detection devices for each seizure type is made.

      Abstract

      Epilepsy affects almost 1% of the population and most of the approximately 20–30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies.
      This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies.

      Keywords

      1. Introduction

      Epilepsy is one of the most common neurological disorders with an incidence of approximately 40–70/100,000 per year in adults [
      • Sander J.W.
      The epidemiology of epilepsy revisited.
      ] and 41–187/100,000 per year in children [
      • Camfield P.
      • Camfield C.
      Incidence, prevalence and aetiology of seizures and epilepsy in children.
      ], being particularly frequent in rural and underdeveloped areas [
      • Sander J.W.
      The epidemiology of epilepsy revisited.
      ,
      • Forsgren L.
      Prevalence of epilepsy in adults in northern Sweden.
      ,
      • Banerjee P.N.
      • Filippi D.
      • Allen Hauser W.
      The descriptive epidemiology of epilepsy – a review.
      ,
      • Mac T.L.
      • Tran D.S.
      • Quet F.
      • Odermatt P.
      • Preux P.M.
      • Tan C.T.
      Epidemiology, aetiology, and clinical management of epilepsy in Asia: a systematic review.
      ]. Almost 47% of patients will become seizure-free with the first anti-seizure medication trial and an additional 14% of patients with a second or third medicine [
      • Kwan P.
      • Brodie M.J.
      Early identification of refractory epilepsy.
      ]. Despite optimal medication management, about 20–30% of patients with epilepsy will continue to have more than one seizure per month, 12% will have weekly seizures, and 8% daily seizures [
      • Forsgren L.
      Prevalence of epilepsy in adults in northern Sweden.
      ,
      • Forsgren L.
      • Beghi E.
      • Oun A.
      • Sillanpää M.
      The epidemiology of epilepsy in Europe – a systematic review.
      ,
      • Sidenvall R.
      • Forsgren L.
      • Heijbel J.
      Prevalence and characteristics of epilepsy in children in northern Sweden.
      ]. Patients with active epilepsy have 4–5 times higher standardized mortality ratios than epilepsy patients who are seizure free, especially in the initial years after a diagnosis of epilepsy [
      • Christensen J.
      • Pedersen C.B.
      • Sidenius P.
      • Olsen J.
      • Vestergaard M.
      Long-term mortality in children and young adults with epilepsy – a population-based cohort study.
      ,
      • Bell G.S.
      • Sinha S.
      • Tisi Jd
      • Stephani C.
      • Scott C.A.
      • Harkness W.F.
      • et al.
      Premature mortality in refractory partial epilepsy: does surgical treatment make a difference?.
      ,
      • Holst A.G.
      • Winkel B.G.
      • Risgaard B.
      • Nielsen J.B.
      • Rasmussen P.V.
      • Haunsø S.
      • et al.
      Epilepsy and risk of death and sudden unexpected death in the young: a nationwide study.
      ,
      • Nashef L.
      • Fish D.R.
      • Sander J.W.
      • Shorvon S.D.
      Incidence of sudden unexpected death in an adult outpatient cohort with epilepsy at a tertiary referral centre.
      ,
      • Sillanpaa M.
      • Shinnar S.
      SUDEP and other causes of mortality in childhood-onset epilepsy.
      ]. The mortality associated with epilepsy has remained stable in the last 50 years despite the introduction of multiple new anti-seizure medications [
      • Loscher W.
      • Schmidt D.
      Modern antiepileptic drug development has failed to deliver: ways out of the current dilemma.
      ,
      • Neligan A.
      • Bell G.S.
      • Shorvon S.D.
      • Sander J.W.
      Temporal trends in the mortality of people with epilepsy: a review.
      ]. Further, seizure unpredictability worsens the quality of life (QOL) of patients with epilepsy and their families [
      • Jacoby A.
      Epilepsy and the quality of everyday life. Findings from a study of people with well-controlled epilepsy.
      ]. Medical treatment tailored to the individual patient's seizures might eventually decrease mortality and increase QOL, but in order to do so, both seizures and response to treatment need to be objectively quantified.
      Currently, seizure tracking relies on subjective patient and family recall and may be influenced by the capacity to identify seizures, the level of awareness during the event, and the ability to remember details afterward. In a series of 451 children with absence seizures, up to 30% of patients with no clinically detected seizures, even during hyperventilation, had seizures detected on 1 h-EEG recordings [
      • Glauser T.A.
      • Cnaan A.
      • Shinnar S.
      • Hirtz D.G.
      • Dlugos D.
      • Masur D.
      • et al.
      Ethosuximide: valproic acid, and lamotrigine in childhood absence epilepsy.
      ,
      • Glauser T.A.
      • Cnaan A.
      • Shinnar S.
      • Hirtz D.G.
      • Dlugos D.
      • Masur D.
      • et al.
      Ethosuximide, valproic acid, and lamotrigine in childhood absence epilepsy: initial monotherapy outcomes at 12 months.
      ]. In an inpatient study the number of seizures detected with EEG was 29 times higher than the number reported by families and 7 times higher than the number clinically observed by nurses [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ]. In several studies patients report only approximately half of their seizures, and even less during sleep [
      • Hoppe C.
      • Poepel A.
      • Elger C.E.
      Epilepsy: accuracy of patient seizure counts.
      ,
      • Blum D.E.
      • Eskola J.
      • Bortz J.J.
      • Fisher R.S.
      Patient awareness of seizures.
      ,
      • Kerling F.
      • Mueller S.
      • Pauli E.
      • Stefan H.
      When do patients forget their seizures? An electroclinical study.
      ]. Seizure detection devices provide more accurate seizure quantification, allowing clinicians to tailor treatment more objectively. In addition, seizure prediction devices may alert when an upcoming seizure is going to occur and may enhance patient and family confidence and improving QOL.
      The main seizure detection modalities are outlined in Table 1. There is extensive literature describing seizure detectors, but limited data on which are optimal for each seizure type [
      • Van de Vel A.
      • Verhaert K.
      • Ceulemans B.
      Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures.
      ]. This manuscript aims to address this gap in knowledge by providing a guide on how to select specific devices for individual seizure types.
      Table 1Types of seizure detection devices.
      Electroencephalogram (EEG)

      Intracranial EEG

      Surface electromyography (sEMG)

      Electrodermal activity (EDA)

      Electrocardiography (EKG)

      Accelerometry (ACM)

      Video detection systems

      Mattress sensors

      Seizure-alert dogs

      Implanted advisory systems

      Cerebral oxygen saturation sensors

      Near infrared spectroscopy (NIRS)

      Skin temperature

      Respiratory monitor

      2. Seizure types

      2.1 Seizure type definitions

      Hughlings Jackson defined an epileptic seizure in 1870 as a “symptom … of an occasional, an excessive and a disorderly discharge of nerve tissue” [
      • Fisher R.S.
      • van Emde Boas W.
      • Blume W.
      • Elger C.
      • Genton P.
      • Lee P.
      • et al.
      Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE).
      ]. The definition of epileptic seizure was reformulated by the ILAE group in 2005 to, “a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain” [
      • Fisher R.S.
      • van Emde Boas W.
      • Blume W.
      • Elger C.
      • Genton P.
      • Lee P.
      • et al.
      Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE).
      ]. Specific seizure types have varying features that may enable detection (Table 1).

      2.2 Different seizures are best captured by different types of sensors

      Knowing the main semiological components of seizures is a basic initial step in the process of selecting the best seizure detection device for each patient. Each seizure type consists of one or more phenomena occurring simultaneously or sequentially [
      • Blume W.T.
      • Lüders H.O.
      • Mizrahi E.
      • Tassinari C.
      • van Emde Boas W.
      • Engel Jr., J.
      Glossary of descriptive terminology for ictal semiology: report of the ILAE task force on classification and terminology.
      ]. In order to evaluate clinical features, the two main components that can be assessed are movement and physiological signals. The movement refers to specific body parts, as the limbs involved in a GTCS, or head or eye deviation. These could be detected by accelerometry, surface electromyography (sEMG), video monitoring, mattress sensors, electro-oculogram, or seizure-alert dogs. The physiological signals include heart rate, respiratory rate, sweating and temperature. These could be detected by electrocardiogram (EKG), sweating by EDA, temperature by a wristband and changes in respiratory rate with a thoracic band. Generalized tonic-clonic seizures (GTCS) may present with violent body movements and often prominent autonomic changes. Therefore, several sensor types recognize GTCS more readily than other seizure types. On the other end of the spectrum, absence are challenging to capture as they consist of a brief decrease in awareness with minimal associated movements (Table 2), and thus are often only picked up by an observer or by EEG.
      Table 2Definitions and main findings of the principal seizure types.
      Seizure typeMain features DefinitionMovementSweatingHR/EKG changes
      AtonicSudden loss of muscle tone lasting 1–2 s, involving head, trunk, jaw or limb musculature±**
      AutonomicAn alteration of the autonomic nervous system, involving cardiovascular, pupillary, gastrointestinal, sudomotor, vasomotor and thermoregulatory functions++
      ClonicSemirhythmic high-amplitude movements that involve the same muscle groups+++
      MyoclonicSudden, brief involuntary single or multiple low-amplitude contraction(s) of muscle(s) or muscle groups+*
      Epileptic spasmSudden flexion and/or extension of predominantly proximal muscles, more sustained than a myoclonic movement but not so prolonged as a tonic seizure+**
      Focal dyscognitive seizureDisturbance of cognition is the most apparent feature accompanied by changes in perception, attention, emotion, memory and executive function±++
      GTCSBilateral symmetric tonic contraction followed by generalized clonic movements of somatic muscles, usually accompanied by autonomic phenomena+++
      HypermotorInvolves predominantly proximal limb or axial muscles producing irregular, sequential semipurposeful movements.+++
      TonicA sustained increase in muscle contraction lasting seconds to minutes+++
      *No data on this particular issue.
      Source: Blume, W.T et al. Glossary of descriptive terminology for ictal semiology: report of the ILAE task force on classification and terminology. Epilepsia, 2001; 42(9): p. 1212–8.
      Legend: EKG: Electrocardiogram. GTCS: Generalized tonic-clonic seizure. HR: Heart rate.
      The ideal seizure detection sensor should be able to detect movement in body parts and changes in physiological signals simultaneously. The system could be even more effective if it also interacted with the patient in an active way that could allow testing for awareness. This could be done with a gaming device that keeps track of the patient's responsiveness while playing. If the patient stops playing, the device could ask the patient to follow some specific commands, and in turn the system would ideally alert a designated caregiver in the event of impaired performance. This could also be done by robot or external device that wirelessly receives the input of movement and physiological signals from the monitoring device. For example a wristband analyzes signals in real time, and when parameters are altered, it will interact with the patient to assert for consciousness. This has an advantage compared to a gaming system in that it could interact with the patient when detecting abnormal physiological parameters during the night, whereas the gaming device's functionality is limited to when the patient is awake. An integral system would hypothetically allow detection of all seizure types, including those considered subtle like absence seizures.

      3. Types of seizure detection sensors

      3.1 Unimodal detection devices

      3.1.1 Electroencephalogram

      Video-EEG has long been considered the gold standard for the diagnosis of seizures. Several groups have developed algorithms for automatic seizure detection based on EEG [
      • Aarabi A.
      • He B.
      Seizure prediction in intracranial EEG: a patient-specific rule-based approach.
      ,
      • Ozdemir N.
      • Yildirim E.
      Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers.
      ,
      • Gotman J.
      Automatic seizure detection: improvements and evaluation.
      ,
      • Gadhoumi K.
      • Gotman J.
      • Lina J.M.
      Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.
      ,
      • Park Y.
      • Luo L.
      • Parhi K.K.
      • Netoff T.
      Seizure prediction with spectral power of EEG using cost-sensitive support vector machines.
      ,
      • Rabbi A.F.
      • Azinfar L.
      • Fazel-Rezai R.
      Seizure prediction using adaptive neuro-fuzzy inference system.
      ]. Most of them tested their algorithms with data from the Freiburg Seizure Prediction and the European Epilepsy Databases [
      • Aarabi A.
      • He B.
      Seizure prediction in intracranial EEG: a patient-specific rule-based approach.
      ,
      • Ozdemir N.
      • Yildirim E.
      Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers.
      ,
      • Park Y.
      • Luo L.
      • Parhi K.K.
      • Netoff T.
      Seizure prediction with spectral power of EEG using cost-sensitive support vector machines.
      ,
      • Rabbi A.F.
      • Azinfar L.
      • Fazel-Rezai R.
      Seizure prediction using adaptive neuro-fuzzy inference system.
      ,
      • Alexandre Teixeira C.
      • Direito B.
      • Bandarabadi M.
      • Le Van Quyen M.
      • Valderrama M.
      • Schelter B.
      • et al.
      Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.
      ,
      • Bandarabadi M.
      • Rasekhi J.
      • Teixeira C.A.
      • Karami M.R.
      • Dourado A.
      On the proper selection of preictal period for seizure prediction.
      ,
      • Parvez M.Z.
      • Paul M.
      Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation.
      ]. Most researchers used data from 2 to 6 electrodes trying to recreate the conditions for ambulatory performance. The Freiburg database uses three electrodes close to the focal area and three remote from it [
      • Aarabi A.
      • He B.
      Seizure prediction in intracranial EEG: a patient-specific rule-based approach.
      ,
      • Ozdemir N.
      • Yildirim E.
      Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers.
      ,
      • Alexandre Teixeira C.
      • Direito B.
      • Bandarabadi M.
      • Le Van Quyen M.
      • Valderrama M.
      • Schelter B.
      • et al.
      Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.
      ,
      • Parvez M.Z.
      • Paul M.
      Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation.
      ].
      Researchers first differentiated the preictal period from the ictal period – which can be challenging as the transition is sometimes subtle [
      • Bandarabadi M.
      • Rasekhi J.
      • Teixeira C.A.
      • Karami M.R.
      • Dourado A.
      On the proper selection of preictal period for seizure prediction.
      ,
      • Wang N.
      • Lyu M.R.
      Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction.
      ]. The preictal period is seizure and patient specific, and its identification requires a training period of the algorithm for the individual patient [
      • Ozdemir N.
      • Yildirim E.
      Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers.
      ,
      • Gadhoumi K.
      • Gotman J.
      • Lina J.M.
      Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.
      ,
      • Park Y.
      • Luo L.
      • Parhi K.K.
      • Netoff T.
      Seizure prediction with spectral power of EEG using cost-sensitive support vector machines.
      ,
      • Bandarabadi M.
      • Rasekhi J.
      • Teixeira C.A.
      • Karami M.R.
      • Dourado A.
      On the proper selection of preictal period for seizure prediction.
      ,
      • Wang N.
      • Lyu M.R.
      Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction.
      ]. Investigators developed an algorithm based on the parameters that yielded the best performance [
      • Gadhoumi K.
      • Gotman J.
      • Lina J.M.
      Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.
      ,
      • Park Y.
      • Luo L.
      • Parhi K.K.
      • Netoff T.
      Seizure prediction with spectral power of EEG using cost-sensitive support vector machines.
      ,
      • Alexandre Teixeira C.
      • Direito B.
      • Bandarabadi M.
      • Le Van Quyen M.
      • Valderrama M.
      • Schelter B.
      • et al.
      Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.
      ,
      • Wang N.
      • Lyu M.R.
      Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction.
      ].
      Seizures detected with this approach were focal dyscognitive seizures, focal without dyscognitive changes, secondarily generalized seizures, and absence seizures [
      • Ozdemir N.
      • Yildirim E.
      Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers.
      ,
      • Gotman J.
      Automatic seizure detection: improvements and evaluation.
      ,
      • Bandarabadi M.
      • Rasekhi J.
      • Teixeira C.A.
      • Karami M.R.
      • Dourado A.
      On the proper selection of preictal period for seizure prediction.
      ,
      • Parvez M.Z.
      • Paul M.
      Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation.
      ,
      • Wang N.
      • Lyu M.R.
      Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction.
      ,
      • Kim H.
      • Rosen J.
      Epileptic seizure detection – an AR model based algorithm for implantable device.
      ,
      • Petersen E.B.
      • Duun-Henriksen J.
      • Mazzaretto A.
      • Kjær T.W.
      • Thomsen C.E.
      • Sorensen H.B.
      Generic single-channel detection of absence seizures.
      ]. Intracranial EEG yielded a sensitivity of 80.5–98.8% and a false detection rate (FDR) of 0.054–1/h, within a prediction horizon of 30–60 min [
      • Aarabi A.
      • He B.
      Seizure prediction in intracranial EEG: a patient-specific rule-based approach.
      ,
      • Ozdemir N.
      • Yildirim E.
      Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers.
      ,
      • Gotman J.
      Automatic seizure detection: improvements and evaluation.
      ,
      • Gadhoumi K.
      • Gotman J.
      • Lina J.M.
      Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.
      ,
      • Park Y.
      • Luo L.
      • Parhi K.K.
      • Netoff T.
      Seizure prediction with spectral power of EEG using cost-sensitive support vector machines.
      ,
      • Rabbi A.F.
      • Azinfar L.
      • Fazel-Rezai R.
      Seizure prediction using adaptive neuro-fuzzy inference system.
      ,
      • Parvez M.Z.
      • Paul M.
      Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation.
      ,
      • Wang N.
      • Lyu M.R.
      Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction.
      ,
      • Luca S.
      • Karsmakers P.
      • Cuppens K.
      • Croonenborghs T.
      • Van de Vel A.
      • Ceulemans B.
      • et al.
      Detecting rare events using extreme value statistics applied to epileptic convulsions in children.
      ]. The data from scalp EEG had a sensitivity of 74–99% and an FDR of 0.28–1/h, with an anticipation time of 16 min [
      • Gotman J.
      Automatic seizure detection: improvements and evaluation.
      ,
      • Alexandre Teixeira C.
      • Direito B.
      • Bandarabadi M.
      • Le Van Quyen M.
      • Valderrama M.
      • Schelter B.
      • et al.
      Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.
      ,
      • Kim H.
      • Rosen J.
      Epileptic seizure detection – an AR model based algorithm for implantable device.
      ,
      • Zandi A.S.
      • Dumont G.A.
      • Javidan M.
      • Tafreshi R.
      Epileptic seizure prediction using variational mixture of Gaussians.
      ,
      • Bandarabadi M.
      • Teixeira C.A.
      • Rasekhi J.
      • Dourado A.
      Epileptic seizure prediction using relative spectral power features.
      ]. A device for detection of absence seizures achieved a sensitivity of 95%, with an FDR of 0.037/h, when tested in an ambulatory setting in one patient [
      • Duun-Henriksen J.
      • Madsen R.E.
      • Remvig L.S.
      • Thomsen C.E.
      • Sorensen H.B.
      • Kjaer T.W.
      Automatic detection of childhood absence epilepsy seizures: toward a monitoring device.
      ]. The electrodes were placed in F7-Fp1, as this yielded the best results in a previous study [
      • Petersen E.B.
      • Duun-Henriksen J.
      • Mazzaretto A.
      • Kjær T.W.
      • Thomsen C.E.
      • Sorensen H.B.
      Generic single-channel detection of absence seizures.
      ].
      Patients must wear scalp electrodes and remain attached to EEG equipment during monitoring, which increases artifact, is impractical, and potentially leads to stigmatization and discomfort [
      • Schulze-Bonhage A.
      • Sales F.
      • Wagner K.
      • Teotonio R.
      • Carius A.
      • Schelle A.
      • et al.
      Views of patients with epilepsy on seizure prediction devices.
      ]. The current devices for ambulatory EEG could be used as a model, as they are light weight and patients can wear them attached to a belt or over the shoulder [
      • Waterhouse E.
      New horizons in ambulatory electroencephalography.
      ]. Several researchers are working on the development of wireless EEG modalities, with few and small electrodes [
      • Luan B.
      • Sun M.
      A simulation study on a single-unit wireless EEG sensor.
      ,
      • Luan B.
      • Jia W.
      • Thirumala P.D.
      • Balzer J.
      • Gao D.
      • Sun M.
      A feasibility study on a single-unit wireless EEG sensor.
      ,
      • Wyckoff S.N.
      • Sherlin L.H.
      • Ford N.L.
      • Dalke D.
      Validation of a wireless dry electrode system for electroencephalography.
      ,
      • Do Valle B.G.
      • Cash S.S.
      • Sodini C.G.
      Wireless behind-the-ear EEG recording device with wireless interface to a mobile device (iPhone/iPod touch).
      ,
      • Mihajlovic V.
      • Grundlehner B.
      • Vullers R.
      • Penders J.
      Wearable, wireless EEG solutions in daily life applications: what are we missing?.
      ,
      • Grant A.C.
      • Abdel-Baki S.G.
      • Omurtag A.
      • Sinert R.
      • Chari G.
      • Malhotra S.
      • et al.
      Diagnostic accuracy of microEEG: a miniature, wireless EEG device.
      ]. A group used four electrodes installed in a 20 cm area and demonstrated that this is feasible and comparable to a 10–20 EEG system [
      • Luan B.
      • Sun M.
      A simulation study on a single-unit wireless EEG sensor.
      ]. The Emotiv EPOC is a low-cost wireless system with 14 electrodes and a battery life of 12 h enabling the device to be worn during the day [
      • Grummett T.S.
      • Leibbrandt R.E.
      • Lewis T.W.
      • DeLosAngeles D.
      • Powers D.M.
      • Willoughby J.O.
      • et al.
      Measurement of neural signals from inexpensive: wireless and dry EEG systems.
      ,
      • David Hairston W.
      • Whitaker K.W.
      • Ries A.J.
      • Vettel J.M.
      • Cortney Bradford J.
      • Kerick S.E.
      • et al.
      Usability of four commercially-oriented EEG systems.
      ]. The B-Alert is a wireless EEG head unit with 21 channels (20 electrodes), in which the data is transmitted to the receiver [
      • Grummett T.S.
      • Leibbrandt R.E.
      • Lewis T.W.
      • DeLosAngeles D.
      • Powers D.M.
      • Willoughby J.O.
      • et al.
      Measurement of neural signals from inexpensive: wireless and dry EEG systems.
      ]. These two systems were compared and B-Alert (in two different versions) was the most similar to a research-grade system and it detected all of the EEG expected features [
      • Grummett T.S.
      • Leibbrandt R.E.
      • Lewis T.W.
      • DeLosAngeles D.
      • Powers D.M.
      • Willoughby J.O.
      • et al.
      Measurement of neural signals from inexpensive: wireless and dry EEG systems.
      ,
      • David Hairston W.
      • Whitaker K.W.
      • Ries A.J.
      • Vettel J.M.
      • Cortney Bradford J.
      • Kerick S.E.
      • et al.
      Usability of four commercially-oriented EEG systems.
      ]. Another wireless system was developed for ambulances and emergency departments with a short preparation time because it is a cap with six channels [
      • Jakab A.
      • Kulkas A.
      • Salpavaara T.
      • Kauppinen P.
      • Verho J.
      • Heikkilä H.
      • et al.
      Novel wireless electroencephalography system with a minimal preparation time for use in emergencies and prehospital care.
      ]. There is also a portable, wireless system that uploads the data to a smartphone [
      • Do Valle B.G.
      • Cash S.S.
      • Sodini C.G.
      Wireless behind-the-ear EEG recording device with wireless interface to a mobile device (iPhone/iPod touch).
      ]. It has two electrodes, one over the area of interest and the other over the mastoid, and it was able to record a seizure [
      • Do Valle B.G.
      • Cash S.S.
      • Sodini C.G.
      Wireless behind-the-ear EEG recording device with wireless interface to a mobile device (iPhone/iPod touch).
      ]. Finally a waterproof, 2 electrodes (1-channel) EEG monitoring device called the EEG Patch TM is able to track seizures for 7 days and allows home monitoring [
      • Lehmkuhle M.
      • Elwood M.
      • Wheeler J.
      • Fisher J.
      • Dudek F.E.
      Development of a discrete, wearable, EEG device for counting seizures (abstract).
      ]. The experience with these devices detecting seizures and their sensitivity compared to standard EEG is not yet published for some of them. They represent promising systems that when linked with an automated detection algorithm could allow for accurate seizure detection.
      Alternatively, intracranial EEG is an invasive procedure that requires close supervision and is associated with risk of infection. In the future, devices with only a few and small electrodes will increase portability, and machine learning algorithms will further automatize the seizure identification process. Large studies targeting homogeneous seizure types with machine learning algorithms will clarify the role of EEG as a potential portable seizure detection device.

      3.1.2 Surface electromyography (sEMG)

      Most seizures have a motor component; therefore the analysis of muscle activity with sEMG is a viable option for seizure detection [
      • Larsen S.N.
      • Conradsen I.
      • Beniczky S.
      • Sorensen H.B.
      Detection of tonic epileptic seizures based on surface electromyography.
      ]. sEMG helps differentiate epileptic seizures from non-epileptic seizures: epileptic seizures have a large proportion of EMG activity in the frequency band above 100–150 Hz [
      • Conradsen I.
      • Beniczky S.
      • Hoppe K.
      • Wolf P.
      • Sorensen H.B.
      Automated algorithm for generalized tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate.
      ,
      • Conradsen I.
      • Wolf P.
      • Sams T.
      • Sorensen H.B.
      • Beniczky S.
      Patterns of muscle activation during generalized tonic and tonic-clonic epileptic seizures.
      ]. sEMG detects muscle activity with as few as one channel; deltoid and anterior tibialis muscles are the preferred placement sites [
      • Conradsen I.
      • Beniczky S.
      • Hoppe K.
      • Wolf P.
      • Sorensen H.B.
      Automated algorithm for generalized tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate.
      ,
      • Conradsen I.
      • Wolf P.
      • Sams T.
      • Sorensen H.B.
      • Beniczky S.
      Patterns of muscle activation during generalized tonic and tonic-clonic epileptic seizures.
      ,
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Jennum P.
      • Sorensen H.B.
      Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection.
      ].
      Tonic stiffening consists of an intense muscle contraction, which allows for early GTCS detection [
      • Larsen S.N.
      • Conradsen I.
      • Beniczky S.
      • Sorensen H.B.
      Detection of tonic epileptic seizures based on surface electromyography.
      ,
      • Conradsen I.
      • Beniczky S.
      • Hoppe K.
      • Wolf P.
      • Sorensen H.B.
      Automated algorithm for generalized tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate.
      ,
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Jennum P.
      • Sorensen H.B.
      Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection.
      ]. In a study, the efficacy of deltoid muscle detection site was better than for the anterior tibialis muscle, with a sensitivity of 100%, mean detection latency (time from beginning of a seizure to its detection) of 13.7 s, and FDR of one alarm per 24 h [
      • Conradsen I.
      • Beniczky S.
      • Hoppe K.
      • Wolf P.
      • Sorensen H.B.
      Automated algorithm for generalized tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate.
      ]. When using only the nocturnal data, the FDR improved to one every 10 nights [
      • Conradsen I.
      • Beniczky S.
      • Hoppe K.
      • Wolf P.
      • Sorensen H.B.
      Automated algorithm for generalized tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate.
      ]. sEMG in the anterior tibialis muscle had a sensitivity of 57%, but the FDR improved to one false alarm in 12 days with a latency of 25 s [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Jennum P.
      • Sorensen H.B.
      Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection.
      ]. The sEMG on tonic seizures, recorded at the deltoid muscle, had a sensitivity of 53–63% and a FDR of 1.49–4.03 [
      • Larsen S.N.
      • Conradsen I.
      • Beniczky S.
      • Sorensen H.B.
      Detection of tonic epileptic seizures based on surface electromyography.
      ]. A recent study placed the sEMG electrodes in the biceps and triceps detecting 95% of GTCs but none of the other seizures (myoclonic, tonic, absence, and focal seizures with or without loss of consciousness) [
      • Szabó C.Á.
      • Morgan L.C.
      • Karkar K.M.
      • Leary L.D.
      • Lie O.V.
      • Girouard M.
      • et al.
      Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings.
      ].
      Disadvantages of sEMG sensors include discomfort when strongly fixed to the skin and potential for detachment [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Jennum P.
      • Sorensen H.B.
      Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection.
      ]. Better results may be achieved when certain parameters are tailored to the individual patient, especially for tonic seizures [
      • Larsen S.N.
      • Conradsen I.
      • Beniczky S.
      • Sorensen H.B.
      Detection of tonic epileptic seizures based on surface electromyography.
      ]. In summary, sEMG reliably detects GTCS and tonic seizures and can potentially detect other seizure types with a motor component.

      3.1.3 Electrodermal activity (EDA)

      Modulation in skin conductance is referred to as EDA, and it reflects the activity of the sympathetic branch of the autonomic nervous system [
      • Poh M.Z.
      • Swenson N.C.
      • Picard R.W.
      A wearable sensor for unobtrusive: long-term assessment of electrodermal activity.
      ,
      • Poh M.Z.
      • Loddenkemper T.
      • Swenson N.C.
      • Goyal S.
      • Madsen J.R.
      • Picard R.W.
      Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor.
      ]. Sweat increases the conductance of an applied current [
      • Poh M.Z.
      • Swenson N.C.
      • Picard R.W.
      A wearable sensor for unobtrusive: long-term assessment of electrodermal activity.
      ]. The device applies direct current to the stratum corneum beneath the electrodes, and measures the EDA in the ventral side of distal forearms [
      • Poh M.Z.
      • Swenson N.C.
      • Picard R.W.
      A wearable sensor for unobtrusive: long-term assessment of electrodermal activity.
      ,
      • Poh M.Z.
      • Loddenkemper T.
      • Swenson N.C.
      • Goyal S.
      • Madsen J.R.
      • Picard R.W.
      Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor.
      ]. The rationale for the use of this device is that epileptic seizures transiently increase EDA [
      • Poh M.Z.
      • Loddenkemper T.
      • Swenson N.C.
      • Goyal S.
      • Madsen J.R.
      • Picard R.W.
      Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor.
      ].
      In a study with seven patients the EDA was significantly elevated immediately after the onset of each EEG seizure, including GTCS and focal dyscognitive seizures [
      • Poh M.Z.
      • Loddenkemper T.
      • Swenson N.C.
      • Goyal S.
      • Madsen J.R.
      • Picard R.W.
      Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor.
      ]. But the change in EDA was higher and remained elevated for a longer period in GTCS compared to focal dyscognitive seizures [
      • Poh M.Z.
      • Loddenkemper T.
      • Swenson N.C.
      • Goyal S.
      • Madsen J.R.
      • Picard R.W.
      Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor.
      ]. In a study including 11 patients, 100% of GTCS had a greater than 2 standard deviation (SD) increase in EDA, but only 86% of focal dyscognitive seizures had it, with a median latency for all seizures of 33 s [
      • Poh M.Z.
      • Loddenkemper T.
      • Reinsberger C.
      • Swenson N.C.
      • Goyal S.
      • Madsen J.R.
      • et al.
      Autonomic changes with seizures correlate with postictal EEG suppression.
      ].
      Studies in adult and pediatric patients have demonstrated a strong correlation between the duration of postictal generalized EEG suppression (PGES) and the degree of EDA response. Children seem to have lower PGES duration, which could explain lower sudden unexplained death in epilepsy (SUDEP) rates, although they have more sympathetic activation and diminished vagal tone when compared to adults [
      • Sarkis R.A.
      • Thome-Souza S.
      • Poh M.Z.
      • Llewellyn N.
      • Klehm J.
      • Madsen J.R.
      • et al.
      Autonomic changes following generalized tonic clonic seizures: an analysis of adult and pediatric patients with epilepsy.
      ,
      • Nickels K.C.
      • Grossardt B.R.
      • Wirrell E.C.
      Epilepsy-related mortality is low in children: a 30-year population-based study in Olmsted County, MN.
      ]. EDA recordings may also be able to help us better understand the pathophysiology of SUDEP [
      • Poh M.Z.
      • Loddenkemper T.
      • Reinsberger C.
      • Swenson N.C.
      • Goyal S.
      • Madsen J.R.
      • et al.
      Autonomic changes with seizures correlate with postictal EEG suppression.
      ,
      • Sarkis R.A.
      • Thome-Souza S.
      • Poh M.Z.
      • Llewellyn N.
      • Klehm J.
      • Madsen J.R.
      • et al.
      Autonomic changes following generalized tonic clonic seizures: an analysis of adult and pediatric patients with epilepsy.
      ].
      Disadvantages include that EDA recording is susceptible to motion and pressure artifacts and it could be uncomfortable or obtrusive. The EDA in the ventral side of the distal forearm is well tolerated, even for long periods [
      • Poh M.Z.
      • Swenson N.C.
      • Picard R.W.
      A wearable sensor for unobtrusive: long-term assessment of electrodermal activity.
      ]. Large studies on continuous ambulatory autonomic monitoring will provide insights to optimize this promising modality.

      3.1.4 Electrocardiogram (EKG)

      Cardiovascular changes are relatively easy to measure, and in patients with epilepsy they are particularly relevant as they may be linked to SUDEP [
      • van Elmpt W.J.
      • Nijsen T.M.
      • Griep P.A.
      • Arends J.B.
      A model of heart rate changes to detect seizures in severe epilepsy.
      ,
      • Opherk C.
      • Coromilas J.
      • Hirsch L.J.
      Heart rate and EKG changes in 102 seizures: analysis of influencing factors.
      ]. EKG can be recorded from a single channel and has a higher signal to noise ratio than EEG [
      • Behbahani S.
      • Dabanloo N.J.
      • Nasrabadi A.M.
      • Teixeira C.A.
      • Dourado A.
      Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses.
      ]. Multiple studies have aimed to characterize heart rate changes before, during, and after seizures [
      • van Elmpt W.J.
      • Nijsen T.M.
      • Griep P.A.
      • Arends J.B.
      A model of heart rate changes to detect seizures in severe epilepsy.
      ,
      • Opherk C.
      • Coromilas J.
      • Hirsch L.J.
      Heart rate and EKG changes in 102 seizures: analysis of influencing factors.
      ,
      • Behbahani S.
      • Dabanloo N.J.
      • Nasrabadi A.M.
      • Teixeira C.A.
      • Dourado A.
      Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses.
      ,
      • Osorio I.
      • Manly B.F.
      Is seizure detection based on EKG clinically relevant?.
      ,
      • Zijlmans M.
      • Flanagan D.
      • Gotman J.
      Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign.
      ,
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot.
      ,
      • van Andel J.
      • Ungureanu C.
      • Aarts R.
      • Leijten F.
      • Arends J.
      Using photoplethysmography in heart rate monitoring of patients with epilepsy.
      ]. Heart rate changes could be explained by increased motor activity, release of catecholamines, sympathetic and parasympathetic shifts, activation of limbic structures, increased neuronal firing, or a combination of these and other unknown factors [
      • van Elmpt W.J.
      • Nijsen T.M.
      • Griep P.A.
      • Arends J.B.
      A model of heart rate changes to detect seizures in severe epilepsy.
      ,
      • Behbahani S.
      • Dabanloo N.J.
      • Nasrabadi A.M.
      • Teixeira C.A.
      • Dourado A.
      Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses.
      ,
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot.
      ,
      • Kerem D.H.
      • Geva A.B.
      Forecasting epilepsy from the heart rate signal.
      ]. Another aspect that must be taken into consideration is that heart rate also depends on the state of vigilance so sensors using heart rate to detect seizures might be affected by this [
      • Sei H.
      • Furuno N.
      • Morita Y.
      Diurnal changes of blood pressure: heart rate and body temperature during sleep in the rat.
      ,
      • Mancia G.
      • Ferrari A.
      • Gregorini L.
      • Parati G.
      • Pomidossi G.
      • Bertinieri G.
      • et al.
      Blood pressure and heart rate variabilities in normotensive and hypertensive human beings.
      ]. The pattern of heart rate changes seems to be patient-specific, reflecting the individual spread and evolution of seizure activity and warrant the development of patient-tailored detection algorithms [
      • van Elmpt W.J.
      • Nijsen T.M.
      • Griep P.A.
      • Arends J.B.
      A model of heart rate changes to detect seizures in severe epilepsy.
      ,
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot.
      ,
      • Kerem D.H.
      • Geva A.B.
      Forecasting epilepsy from the heart rate signal.
      ,
      • Leutmezer F.
      • Schernthaner C.
      • Lurger S.
      • Pötzelberger K.
      • Baumgartner C.
      Electrocardiographic changes at the onset of epileptic seizures.
      ].
      EKG has been used to detect focal seizures, secondarily generalized seizures and GTCS [
      • Jansen K.
      • Varon C.
      • Van Huffel S.
      • Lagae L.
      Peri-ictal ECG changes in childhood epilepsy: implications for detection systems.
      ]. EKG abnormalities have been linearly correlated to electrocorticogram (ECoG) seizure severity, proving the feasibility of EKG as a seizure detection device [
      • Osorio I.
      • Manly B.F.
      Is seizure detection based on EKG clinically relevant?.
      ]. Ictal tachycardia was more prominent when arising from the right hemisphere [
      • Opherk C.
      • Coromilas J.
      • Hirsch L.J.
      Heart rate and EKG changes in 102 seizures: analysis of influencing factors.
      ,
      • Leutmezer F.
      • Schernthaner C.
      • Lurger S.
      • Pötzelberger K.
      • Baumgartner C.
      Electrocardiographic changes at the onset of epileptic seizures.
      ]. In contrast, short myoclonic seizures often did not produce heart changes [
      • van Elmpt W.J.
      • Nijsen T.M.
      • Griep P.A.
      • Arends J.B.
      A model of heart rate changes to detect seizures in severe epilepsy.
      ]. In a large study, 73% of focal seizures had heart rate increase, and in 23% it preceded EEG onset [
      • Zijlmans M.
      • Flanagan D.
      • Gotman J.
      Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign.
      ]. In another study in children, ictal tachycardia was present in 70% of focal seizures (temporal or frontal onset), but not in generalized seizures [
      • Jansen K.
      • Varon C.
      • Van Huffel S.
      • Lagae L.
      Peri-ictal ECG changes in childhood epilepsy: implications for detection systems.
      ]. One interesting study found that heart rate increased from the preictal to the ictal period in 74% of the patients and it was higher in seizures with secondary generalization than in complex partial (focal dyscognitive) seizures [
      • Nilsen K.B.
      • Haram M.
      • Tangedal S.
      • Sand T.
      • Brodtkorb E.
      Is elevated pre-ictal heart rate associated with secondary generalization in partial epilepsy?.
      ]. One of the few studies that included generalized seizures detected heart rate changes in 35% of seizures, either GTCS or secondarily generalized seizures [
      • Opherk C.
      • Coromilas J.
      • Hirsch L.J.
      Heart rate and EKG changes in 102 seizures: analysis of influencing factors.
      ]. The sensitivity of the automated detection algorithms was 90–98% in seizures with heart rate changes, with a greater than 50% positive predictive value [
      • van Elmpt W.J.
      • Nijsen T.M.
      • Griep P.A.
      • Arends J.B.
      A model of heart rate changes to detect seizures in severe epilepsy.
      ,
      • Osorio I.
      Automated seizure detection using EKG.
      ]. The latency was between 0.8 s and 10 min for all seizure types [
      • Behbahani S.
      • Dabanloo N.J.
      • Nasrabadi A.M.
      • Teixeira C.A.
      • Dourado A.
      Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses.
      ,
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot.
      ,
      • Kerem D.H.
      • Geva A.B.
      Forecasting epilepsy from the heart rate signal.
      ,
      • Leutmezer F.
      • Schernthaner C.
      • Lurger S.
      • Pötzelberger K.
      • Baumgartner C.
      Electrocardiographic changes at the onset of epileptic seizures.
      ,
      • Osorio I.
      Automated seizure detection using EKG.
      ].
      Regarding ambulatory monitoring, a group developed a wireless device for real time detection of seizures that allows unobtrusive monitoring with good mobility. This may be effective for detection of tonic-clonic, tonic, clonic and hypermotor seizures [
      • Massé F.
      • van Bussel M.
      • Serteyn A.
      • Arends J.
      • Penders J.
      Miniaturized wireless ECG monitor for real-time detection of epileptic seizures.
      ]. The monitor is composed of a wireless sensor board, ultra-low power EKG sensor readout, accelerometer, and micro-secure digital-card. The detection algorithm has a sensitivity of 99.8% and positive predictive value of 99.8% [
      • Romero I.
      • Grundlehner B.
      • Penders J.
      Robust beat detector for ambulatory cardiac monitoring.
      ]. The device with two electrodes was placed on the left arm of four healthy volunteers who considered it comfortable and reported no inconvenience during sleep [
      • Massé F.
      • van Bussel M.
      • Serteyn A.
      • Arends J.
      • Penders J.
      Miniaturized wireless ECG monitor for real-time detection of epileptic seizures.
      ]. A smartphone application monitors heart rate remotely based on skin color changes [
      • Kwon S.
      • Kim H.
      • Park K.S.
      Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone.
      ]. Since cardiac pulse leads to subtle changes in skin color, a photoplethysmographic signal can be measured recording the face with the front facing-camera of a smartphone. Heart rates were measured for a minute with good accuracy in adults who held the smartphone 30 centimeters away from their face without moving [
      • Kwon S.
      • Kim H.
      • Park K.S.
      Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone.
      ]. This technique could ultimately prove useful in patients with seizures having a motor component. Another option utilizes cardiac-based activation vagus nerve stimulation as part of a commercially available closed-loop system [
      • Boon P.
      • Vonck K.
      • van Rijckevorsel K.
      • El Tahry R.
      • Elger C.E.
      • Mullatti N.
      • et al.
      A prospective, multicenter study of cardiac-based seizure detection to activate vagus nerve stimulation.
      ]. This cardiac-based seizure detection algorithm had a sensitivity of 80%. The effect on seizure frequency was moderate but there was significant improvement in QOL [
      • Boon P.
      • Vonck K.
      • van Rijckevorsel K.
      • El Tahry R.
      • Elger C.E.
      • Mullatti N.
      • et al.
      A prospective, multicenter study of cardiac-based seizure detection to activate vagus nerve stimulation.
      ].
      Some disadvantages of EKG as a seizure detection signal include the low specificity of changes in heart rate [
      • van Elmpt W.J.
      • Nijsen T.M.
      • Griep P.A.
      • Arends J.B.
      A model of heart rate changes to detect seizures in severe epilepsy.
      ,
      • Ramgopal S.
      • Thome-Souza S.
      • Jackson M.
      • Kadish N.E.
      • Sánchez Fernández I.
      • Klehm J.
      • et al.
      Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy.
      ], low stability of electrodes, and discomfort with long-term use, but these could avoided with wireless, video, or VNS activated devices. The findings above and the development of an ambulatory device demonstrate that automated EKG seizure detection is possible, particularly when parameters are tailored to the individual patient.

      3.1.5 Accelerometry (ACM)

      ACM has been used for motor seizures as it detects changes in velocity and direction [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ,
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ,
      • Nijsen T.M.
      • Aarts R.M.
      • Cluitmans P.J.
      • Griep P.A.
      Time-frequency analysis of accelerometry data for detection of myoclonic seizures.
      ]. The signal is recorded by means of a three-axis motion/accelerometer sensor, a microprocessor, and a small, rechargeable battery, usually placed on a limb [
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ,
      • Van de Vel A.
      • Cuppens K.
      • Bonroy B.
      • Milosevic M.
      • Van Huffel S.
      • Vanrumste B.
      • et al.
      Long-term home monitoring of hypermotor seizures by patient-worn accelerometers.
      ,
      • Kramer U.
      • Kipervasser S.
      • Shlitner A.
      • Kuzniecky R.
      A novel portable seizure detection alarm system: preliminary results.
      ,
      • Nijsen T.M.
      • Aarts R.M.
      • Arends J.B.
      • Cluitmans P.J.
      Model for arm movements during myoclonic seizures.
      ]. The main challenge is to differentiate seizures from normal, daily, repetitive movements [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ,
      • Dalton A.
      • Patel S.
      • Chowdhury A.R.
      • Welsh M.
      • Pang T.
      • Schachter S.
      • et al.
      Development of a body sensor network to detect motor patterns of epileptic seizures.
      ]. Some systems have a cancel button and this gives the opportunity to indicate that a movement was a false alarm, avoiding a false-positive alert to the caregiver [
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ].
      This modality was able to detect focal seizures with minimal motor component, GTCS, secondarily generalized seizures, myoclonic, clonic, tonic and hypermotor seizures [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ,
      • Van de Vel A.
      • Verhaert K.
      • Ceulemans B.
      Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures.
      ,
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ,
      • Nijsen T.M.
      • Aarts R.M.
      • Cluitmans P.J.
      • Griep P.A.
      Time-frequency analysis of accelerometry data for detection of myoclonic seizures.
      ,
      • Kramer U.
      • Kipervasser S.
      • Shlitner A.
      • Kuzniecky R.
      A novel portable seizure detection alarm system: preliminary results.
      ,
      • Schulc E.
      • Unterberger I.
      • Saboor S.
      • Hilbe J.
      • Ertl M.
      • Ammenwerth E.
      • et al.
      Measurement and quantification of generalized tonic-clonic seizures in epilepsy patients by means of accelerometry--an explorative study.
      ,
      • Patterson A.L.
      • Mudigoudar B.
      • Fulton S.
      • McGregor A.
      • Poppel K.V.
      • Wheless M.C.
      • et al.
      SmartWatch by SmartMonitor: assessment of seizure detection efficacy for various seizure types in children, a large prospective single-center study.
      ]. Clonic seizures present with a burst-like pattern, which was better identified and discriminated from other movements [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ,
      • Schulc E.
      • Unterberger I.
      • Saboor S.
      • Hilbe J.
      • Ertl M.
      • Ammenwerth E.
      • et al.
      Measurement and quantification of generalized tonic-clonic seizures in epilepsy patients by means of accelerometry--an explorative study.
      ]. Tonic seizures are block-shaped because the acceleration is almost constant. They resemble slow normal movements, which makes them harder to identify [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ,
      • Nijsen T.M.
      • Aarts R.M.
      • Cluitmans P.J.
      • Griep P.A.
      Time-frequency analysis of accelerometry data for detection of myoclonic seizures.
      ]. Focal dyscognitive seizures without motor phenomena and absence seizures were not detected [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ,
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ]. Sensitivity ranges between 16 and 100%; one study had a FDR of 0.2/day [
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ,
      • Van de Vel A.
      • Cuppens K.
      • Bonroy B.
      • Milosevic M.
      • Van Huffel S.
      • Vanrumste B.
      • et al.
      Long-term home monitoring of hypermotor seizures by patient-worn accelerometers.
      ,
      • Kramer U.
      • Kipervasser S.
      • Shlitner A.
      • Kuzniecky R.
      A novel portable seizure detection alarm system: preliminary results.
      ,
      • Dalton A.
      • Patel S.
      • Chowdhury A.R.
      • Welsh M.
      • Pang T.
      • Schachter S.
      • et al.
      Development of a body sensor network to detect motor patterns of epileptic seizures.
      ,
      • Schulc E.
      • Unterberger I.
      • Saboor S.
      • Hilbe J.
      • Ertl M.
      • Ammenwerth E.
      • et al.
      Measurement and quantification of generalized tonic-clonic seizures in epilepsy patients by means of accelerometry--an explorative study.
      ,
      • Patterson A.L.
      • Mudigoudar B.
      • Fulton S.
      • McGregor A.
      • Poppel K.V.
      • Wheless M.C.
      • et al.
      SmartWatch by SmartMonitor: assessment of seizure detection efficacy for various seizure types in children, a large prospective single-center study.
      ,
      • Jallon P.
      • Bonnet S.
      • Antonakios M.
      • Guillemaud R.
      Detection system of motor epileptic seizures through motion analysis with 3D accelerometers.
      ]. Seizures were detected 9–60 s after seizure onset [
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ,
      • Kramer U.
      • Kipervasser S.
      • Shlitner A.
      • Kuzniecky R.
      A novel portable seizure detection alarm system: preliminary results.
      ]. The same accuracy for nocturnal and daytime seizures was achieved [
      • Nijsen T.M.
      • Arends J.B.
      • Griep P.A.
      • Cluitmans P.J.
      The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.
      ,
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ]. One study validated the system in a home environment detecting 78.5% of the seizures reported by parents, with 0.6 false alarms per night [
      • Jallon P.
      • Bonnet S.
      • Antonakios M.
      • Guillemaud R.
      Detection system of motor epileptic seizures through motion analysis with 3D accelerometers.
      ].
      Some disadvantages include that the system is restricted to seizures with a motor component, and that seizures are not detected when there is an obstacle to free limb movement [
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ]. More studies in ambulatory settings are needed, as most studies have been in epilepsy monitoring units where movements might be limited [
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Kramer U.
      • Kipervasser S.
      • Shlitner A.
      • Kuzniecky R.
      A novel portable seizure detection alarm system: preliminary results.
      ]. This modality has good sensitivity with good night detection rates, and most patients and families found the device user-friendly [
      • Beniczky S.
      • Polster T.
      • Kjaer T.W.
      • Hjalgrim H.
      Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study.
      ,
      • Lockman J.
      • Fisher R.S.
      • Olson D.M.
      Detection of seizure-like movements using a wrist accelerometer.
      ,
      • Schulc E.
      • Unterberger I.
      • Saboor S.
      • Hilbe J.
      • Ertl M.
      • Ammenwerth E.
      • et al.
      Measurement and quantification of generalized tonic-clonic seizures in epilepsy patients by means of accelerometry--an explorative study.
      ].

      3.1.6 Video detection systems

      Automatic video detection systems use velocity, area, duration, rotation, oscillation, angular speed, and/or displacement (motion trajectory) to detect seizures [
      • Pediaditis M.
      • Tsiknakis M.
      • Leitgeb N.
      Vision-based motion detection: analysis and recognition of epileptic seizures – a systematic review.
      ,
      • Lu H.
      • Pan Y.
      • Mandal B.
      • Eng H.L.
      • Guan C.
      • Chan D.W.
      Quantifying limb movements in epileptic seizures through color-based video analysis.
      ,
      • Cuppens K.
      • Chen C.W.
      • Wong K.B.
      • Van de Vel A.
      • Lagae L.
      • Ceulemans B.
      • et al.
      Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video.
      ,
      • Kalitzin S.
      • Petkov G.
      • Velis D.
      • Vledder B.
      • Lopes da Silva F.
      Automatic segmentation of episodes containing epileptic clonic seizures in video sequences.
      ,
      • Mandal B.
      • Eng H.L.
      • Lu H.
      • Chan D.W.
      • Ng Y.L.
      Non-intrusive head movement analysis of videotaped seizures of epileptic origin.
      ]. The underlying concept is to detect complex motor patterns by automatic interpretation of video data [
      • Pediaditis M.
      • Tsiknakis M.
      • Leitgeb N.
      Vision-based motion detection: analysis and recognition of epileptic seizures – a systematic review.
      ]. The systems have been classified as marker-based or marker-free, depending on whether the cameras track detectable markers placed in relevant places [
      • Pediaditis M.
      • Tsiknakis M.
      • Leitgeb N.
      Vision-based motion detection: analysis and recognition of epileptic seizures – a systematic review.
      ].
      Seizure types that can be captured by video include focal, hypermotor, myoclonic, and clonic [
      • Pediaditis M.
      • Tsiknakis M.
      • Leitgeb N.
      Vision-based motion detection: analysis and recognition of epileptic seizures – a systematic review.
      ,
      • Lu H.
      • Pan Y.
      • Mandal B.
      • Eng H.L.
      • Guan C.
      • Chan D.W.
      Quantifying limb movements in epileptic seizures through color-based video analysis.
      ,
      • Cuppens K.
      • Chen C.W.
      • Wong K.B.
      • Van de Vel A.
      • Lagae L.
      • Ceulemans B.
      • et al.
      Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video.
      ,
      • Rémi J.
      • Cunha J.P.
      • Vollmar C.
      • Topçuoğlu ÖB
      • Meier A.
      • Ulowetz S.
      • et al.
      Quantitative movement analysis differentiates focal seizures characterized by automatisms.
      ]. Myoclonic seizures are detected with good sensitivity and specificity with a marker-based system using spatio-temporal interest points [
      • Cuppens K.
      • Chen C.W.
      • Wong K.B.
      • Van de Vel A.
      • Lagae L.
      • Ceulemans B.
      • et al.
      Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video.
      ]. Reference markers could be placed on the head, trunk and extremities to asses for movement when evaluated with infrared light by a video system. This was done with frontal and temporal lobe seizures to evaluate for the lateralizing value of ictal head turning [
      • Rémi J.
      • Wagner P.
      • O’Dwyer R.
      • Silva Cunha J.P.
      • Vollmar C.
      • Krotofil I.
      • et al.
      Ictal head turning in frontal and temporal lobe epilepsy.
      ,
      • O’Dwyer R.
      • Silva Cunha J.P.
      • Vollmar C.
      • Mauerer C.
      • Feddersen B.
      • Burgess R.C.
      • et al.
      Lateralizing significance of quantitative analysis of head movements before secondary generalization of seizures of patients with temporal lobe epilepsy.
      ]. The overall sensitivity varies from 75 to 100%, positive predictive value over 85%, and specificity of 53–93% [
      • Pediaditis M.
      • Tsiknakis M.
      • Leitgeb N.
      Vision-based motion detection: analysis and recognition of epileptic seizures – a systematic review.
      ,
      • Lu H.
      • Pan Y.
      • Mandal B.
      • Eng H.L.
      • Guan C.
      • Chan D.W.
      Quantifying limb movements in epileptic seizures through color-based video analysis.
      ,
      • Cuppens K.
      • Chen C.W.
      • Wong K.B.
      • Van de Vel A.
      • Lagae L.
      • Ceulemans B.
      • et al.
      Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video.
      ,
      • Kalitzin S.
      • Petkov G.
      • Velis D.
      • Vledder B.
      • Lopes da Silva F.
      Automatic segmentation of episodes containing epileptic clonic seizures in video sequences.
      ]. Reported latencies range from 4.6 to 21.4 s [
      • Lu H.
      • Pan Y.
      • Mandal B.
      • Eng H.L.
      • Guan C.
      • Chan D.W.
      Quantifying limb movements in epileptic seizures through color-based video analysis.
      ].
      Marker-based devices present with the shortcoming that sensors can be uncomfortable or dislocate over time [
      • Lu H.
      • Pan Y.
      • Mandal B.
      • Eng H.L.
      • Guan C.
      • Chan D.W.
      Quantifying limb movements in epileptic seizures through color-based video analysis.
      ,
      • Mandal B.
      • Eng H.L.
      • Lu H.
      • Chan D.W.
      • Ng Y.L.
      Non-intrusive head movement analysis of videotaped seizures of epileptic origin.
      ]. Marker-free systems detect only seizures with a motor component, and they are more limited to the area covered by video: the patient must be visible and properly placed [
      • Pediaditis M.
      • Tsiknakis M.
      • Leitgeb N.
      Vision-based motion detection: analysis and recognition of epileptic seizures – a systematic review.
      ]. Seizure detection based on video is feasible, but it recognizes mainly seizures with large movements.

      3.1.7 Mattress sensors

      Mattress sensors were developed because most cases of SUDEP occur in patients with GTCS while unsupervised in bed at night [
      • Lamberts R.J.
      • Thijs R.D.
      • Laffan A.
      • Langan Y.
      • Sander J.W.
      Sudden unexpected death in epilepsy: people with nocturnal seizures may be at highest risk.
      ,
      • Carlson C.
      • Arnedo V.
      • Cahill M.
      • Devinsky O.
      Detecting nocturnal convulsions: efficacy of the MP5 monitor.
      ,
      • Fulton S.
      • Poppel K.V.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of 2 bed alarms for detection of nocturnal seizures.
      ,
      • Langan Y.
      • Nashef L.
      • Sander J.W.
      Case-control study of SUDEP.
      ,
      • Poppel K.V.
      • Fulton S.P.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of the Emfit movement monitor.
      ]. These devices consist of a sensor placed under the patient's mattress and connected to a monitor. The sensor alerts the family when it detects a stimulus above the set threshold [
      • Carlson C.
      • Arnedo V.
      • Cahill M.
      • Devinsky O.
      Detecting nocturnal convulsions: efficacy of the MP5 monitor.
      ,
      • Fulton S.
      • Poppel K.V.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of 2 bed alarms for detection of nocturnal seizures.
      ,
      • Poppel K.V.
      • Fulton S.P.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of the Emfit movement monitor.
      ]. The MP5 bed seizure monitor detects movement and noise frequency and intensity, with a minimum patient weight of 25 kg [
      • Carlson C.
      • Arnedo V.
      • Cahill M.
      • Devinsky O.
      Detecting nocturnal convulsions: efficacy of the MP5 monitor.
      ]. The ST-2 bed alarm mainly detects abnormal bed motion in patients weighting more than 6.4 kg [
      • Fulton S.
      • Poppel K.V.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of 2 bed alarms for detection of nocturnal seizures.
      ]. The Emfit monitor detects micro-movements, without a minimum weight [
      • Poppel K.V.
      • Fulton S.P.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of the Emfit movement monitor.
      ].
      For GTCS during sleep the MP5 sensitivity was 62.5% and specificity 90% [
      • Carlson C.
      • Arnedo V.
      • Cahill M.
      • Devinsky O.
      Detecting nocturnal convulsions: efficacy of the MP5 monitor.
      ]. In another study including multiple seizure types, the device only detected one GTCS during sleep (16.7%) [
      • Fulton S.
      • Poppel K.V.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of 2 bed alarms for detection of nocturnal seizures.
      ]. The ST-2 did not detect any of 20 nocturnal seizures. It only detected one focal dyscognitive seizure with motor phenomena of the 26 seizures recorded in awake patients, with an overall sensitivity of 2.2%. These two devices were designed to detect GTCS, but their sensitivity is suboptimal for seizures overall [
      • Fulton S.
      • Poppel K.V.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of 2 bed alarms for detection of nocturnal seizures.
      ]. The Emfit sensitivity was 30% during daytime and 85%during sleep for GTCS [
      • Poppel K.V.
      • Fulton S.P.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of the Emfit movement monitor.
      ].
      Most of these devices have the disadvantages of having a weight restriction, detecting only seizures with rhythmic movements, and a low sensitivity [
      • Fulton S.
      • Poppel K.V.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of 2 bed alarms for detection of nocturnal seizures.
      ,
      • Poppel K.V.
      • Fulton S.P.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of the Emfit movement monitor.
      ]. Mattress devices allow for adjustment of the parameters to compensate for individual differences in movement during sleep [
      • Fulton S.
      • Poppel K.V.
      • McGregor A.
      • Ellis M.
      • Patters A.
      • Wheless J.
      Prospective study of 2 bed alarms for detection of nocturnal seizures.
      ]. Individual calibration and testing over a couple of nights in a home setting are recommended [
      • Carlson C.
      • Arnedo V.
      • Cahill M.
      • Devinsky O.
      Detecting nocturnal convulsions: efficacy of the MP5 monitor.
      ]. None of these systems performed as well as video EEG in detecting nocturnal seizures but this is a step in the right direction for a much needed device.

      3.1.8 Seizure-alert dogs

      Seizure-alert dogs are those that display some attention-getting behavior prior to human appreciation of an impending seizure event [
      • Brown S.W.
      • Goldstein L.H.
      Can seizure-alert dogs predict seizures?.
      ,
      • Kirton A.
      • Wirrell E.
      • Zhang J.
      • Hamiwka L.
      Seizure-alerting and -response behaviors in dogs living with epileptic children.
      ,
      • Dalziel D.J.
      • Uthman B.M.
      • Mcgorray S.P.
      • Reep R.L.
      Seizure-alert dogs: a review and preliminary study.
      ]. While the trigger to which these animals respond is not completely understood, it is believed that they alert to subtle human behavioral changes [
      • Brown S.W.
      • Goldstein L.H.
      Can seizure-alert dogs predict seizures?.
      ,
      • Kirton A.
      • Wirrell E.
      • Zhang J.
      • Hamiwka L.
      Seizure-alerting and -response behaviors in dogs living with epileptic children.
      ,
      • Strong V.
      • Brown S.
      • Huyton M.
      • Coyle H.
      Effect of trained seizure alert dogs on frequency of tonic-clonic seizures.
      ]. Hypothetically they may be responding to changes in human respiratory or heart rate or even olfactory phenomena, perhaps pheromone production [
      • Brown S.W.
      • Goldstein L.H.
      Can seizure-alert dogs predict seizures?.
      ,
      • Dalziel D.J.
      • Uthman B.M.
      • Mcgorray S.P.
      • Reep R.L.
      Seizure-alert dogs: a review and preliminary study.
      ]. They are able to alert from 30 s to 45 min before seizure onset [
      • Dalziel D.J.
      • Uthman B.M.
      • Mcgorray S.P.
      • Reep R.L.
      Seizure-alert dogs: a review and preliminary study.
      ,
      • Strong V.
      • Brown S.
      • Huyton M.
      • Coyle H.
      Effect of trained seizure alert dogs on frequency of tonic-clonic seizures.
      ,
      • Strong V.
      • Brown S.W.
      • Walker R.
      Seizure-alert dogs – fact or fiction?.
      ].
      Seizure-alert dogs have been reported to detect atonic, focal dyscognitive, and GTC seizures [
      • Brown S.W.
      • Goldstein L.H.
      Can seizure-alert dogs predict seizures?.
      ,
      • Strong V.
      • Brown S.W.
      • Walker R.
      Seizure-alert dogs – fact or fiction?.
      ,
      • Ortiz R.
      • Liporace J.
      Seizure-alert dogs: observations from an inpatient video/EEG unit.
      ]. One of the few seizures detected by a seizure-alert dog with concomitant video EEG monitoring was focal dyscognitive [
      • Ortiz R.
      • Liporace J.
      Seizure-alert dogs: observations from an inpatient video/EEG unit.
      ]. One study reported a median sensitivity estimate of 80% and specificity of 100% [
      • Kirton A.
      • Wirrell E.
      • Zhang J.
      • Hamiwka L.
      Seizure-alerting and -response behaviors in dogs living with epileptic children.
      ]. Another study found a 43% mean reduction in seizure frequency with the use of seizure-alert dogs, hypothetically due to diminished stress [
      • Strong V.
      • Brown S.
      • Huyton M.
      • Coyle H.
      Effect of trained seizure alert dogs on frequency of tonic-clonic seizures.
      ].
      There are few reports of EEG or other monitoring concomitant to the dogs’ behavior and most articles used diaries or questionnaires as the gold-standard, raising some skepticism on the reliability of the information [
      • Ortiz R.
      • Liporace J.
      Seizure-alert dogs: observations from an inpatient video/EEG unit.
      ,
      • Doherty M.J.
      • Haltiner A.M.
      Wag the dog: skepticism on seizure alert canines.
      ]. Other difficulties are that seizure-alert dogs also respond to psychogenic seizures, as reported on at least seven well documented cases, and they are not able to monitor patients during their own sleep [
      • Ramgopal S.
      • Thome-Souza S.
      • Jackson M.
      • Kadish N.E.
      • Sánchez Fernández I.
      • Klehm J.
      • et al.
      Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy.
      ,
      • Ortiz R.
      • Liporace J.
      Seizure-alert dogs: observations from an inpatient video/EEG unit.
      ,
      • Krauss G.L.
      • Choi J.S.
      • Lesser R.P.
      Pseudoseizure dogs.
      ]. Companionship seems to be one of the primary benefits and this could lead to the increase in QOL [
      • Strong V.
      • Brown S.W.
      • Walker R.
      Seizure-alert dogs – fact or fiction?.
      ,
      • Ortiz R.
      • Liporace J.
      Seizure-alert dogs: observations from an inpatient video/EEG unit.
      ,
      • Doherty M.J.
      • Haltiner A.M.
      Wag the dog: skepticism on seizure alert canines.
      ]. To date, no rigorous data confirms whether seizure prediction by seizure-alert dogs is better than chance [
      • Brown S.W.
      • Goldstein L.H.
      Can seizure-alert dogs predict seizures?.
      ].

      3.1.9 Cerebral oxygen saturation sensors

      Increased cerebral blood flood precedes the onset of a clinical seizure in temporal lobe epilepsy by approximately 20 min [
      • Weinand M.E.
      • Carter L.P.
      • Patton D.D.
      • Oommen K.J.
      • Labiner D.M.
      • Talwar D.
      Long-term surface cortical cerebral blood flow monitoring in temporal lobe epilepsy.
      ]. This finding was confirmed by single-proton emission computed tomography (SPECT) [
      • Federico P.
      • Abbott D.F.
      • Briellmann R.S.
      • Harvey A.S.
      • Jackson G.D.
      Functional MRI of the pre-ictal state.
      ]. One study placed transcutaneous regional cerebral oxygen saturation (rSO2) sensors on each side of the forehead in five patients with GTCS, and compared this to video-EEG. The mean rSO2 value significantly increased in the preictal period by at least 3 SDs in 4 out of the 7 registered GTCS, on average 18 min prior to EEG seizure onset [
      • Moseley B.D.
      • Britton J.W.
      • So E.
      Increased cerebral oxygenation precedes generalized tonic clonic seizures.
      ].
      Larger studies will clarify the relevance of this approach in other seizure types. These findings suggest that cerebral oxygen saturation sensors could have a role as automatic prediction devices, at least in GTCS, with a prolonged time of detection before seizure onset [
      • Moseley B.D.
      • Britton J.W.
      • So E.
      Increased cerebral oxygenation precedes generalized tonic clonic seizures.
      ].

      3.1.10 Near-infrared spectroscopy (NIRS)

      Wavelengths used in NIRS measure cerebral oxygen saturation by using the specific absorption properties of tissues in the near infrared range [
      • Seyal M.
      Frontal hemodynamic changes precede EEG onset of temporal lobe seizures.
      ,
      • Roche-Labarbe N.
      • Zaaimi B.
      • Berquin P.
      • Nehlig A.
      • Grebe R.
      • Wallois F.
      NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children.
      ]. The spectrophotometer, typically placed on the forehead [
      • Seyal M.
      Frontal hemodynamic changes precede EEG onset of temporal lobe seizures.
      ,
      • Roche-Labarbe N.
      • Zaaimi B.
      • Berquin P.
      • Nehlig A.
      • Grebe R.
      • Wallois F.
      NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children.
      ,
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.
      ], emits light into the tissue from the surface of the scalp, and then collects it from a detector close to the emitter [
      • Roche-Labarbe N.
      • Zaaimi B.
      • Berquin P.
      • Nehlig A.
      • Grebe R.
      • Wallois F.
      NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children.
      ].
      Seizures analyzed with this approach are mainly focal, including focal dyscognitive, focal without impairment of consciousness, and focal with secondary generalization, but also absence seizures [
      • Seyal M.
      Frontal hemodynamic changes precede EEG onset of temporal lobe seizures.
      ,
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.
      ]. One study in temporal lobe seizures found an increase in rSO2 in the preictal period, followed by a decline around seizure onset, and then a postictal increase [
      • Seyal M.
      Frontal hemodynamic changes precede EEG onset of temporal lobe seizures.
      ]. The mean preictal rSO2 increase was 7% above baseline, and 5 min before seizure onset [
      • Seyal M.
      Frontal hemodynamic changes precede EEG onset of temporal lobe seizures.
      ]. A study in children with generalized spike and wave discharges had similar results [
      • Roche-Labarbe N.
      • Zaaimi B.
      • Berquin P.
      • Nehlig A.
      • Grebe R.
      • Wallois F.
      NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children.
      ]. The authors revealed that frontal generalized spike and wave discharges were associated with moderate oxygenation 10 s before the discharge, followed by a strong deoxygenation, then a second increase in oxygenation, and a return to baseline [
      • Roche-Labarbe N.
      • Zaaimi B.
      • Berquin P.
      • Nehlig A.
      • Grebe R.
      • Wallois F.
      NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children.
      ]. In another study, the hemodynamic changes in the frontal lobe during seizures were only slightly higher than during non-seizure activities [
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.
      ].
      One of the disadvantages of this system is that the sensor pad might be difficult to wear from a cosmetic point of view, since it must be surrounded by a black cloth so that no external light interferes with the NIRS signal [
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.
      ]. In addition, one of its biggest challenges is developing a generic algorithm, since there is a huge diversity in hemodynamic changes amongst patients [
      • Jeppesen J.
      • Beniczky S.
      • Johansen P.
      • Sidenius P.
      • Fuglsang-Frederiksen A.
      Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.
      ]. The findings suggest that NIRS might be a noninvasive detection system, at least for focal seizures, with a patient specific algorithm [
      • Seyal M.
      Frontal hemodynamic changes precede EEG onset of temporal lobe seizures.
      ].

      3.1.11 Implanted advisory system

      This system was developed to predict and quantify seizures in adults with refractory focal seizures [
      • Cook M.J.
      • O’Brien T.J.
      • Berkovic S.F.
      • Murphy M.
      • Morokoff A.
      • Fabinyi G.
      • et al.
      Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
      ]. Two silicon leads, each with eight contacts, were placed over the quadrant suspected to contain the epileptogenic zone [
      • Cook M.J.
      • O’Brien T.J.
      • Berkovic S.F.
      • Murphy M.
      • Morokoff A.
      • Fabinyi G.
      • et al.
      Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
      ]. A hand-held device analyzed the recorded EEG based on a patient specific algorithm, developed during the data collection phase. This device transmitted audible and visual signals showing the likelihood of seizure occurrence minutes to hours before it actually occurred [
      • Cook M.J.
      • O’Brien T.J.
      • Berkovic S.F.
      • Murphy M.
      • Morokoff A.
      • Fabinyi G.
      • et al.
      Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
      ].
      The mean sensitivity after four months of implantation was 66% (10 patients). The usefulness of seizure prediction was inconclusive, and the variability in warning times and difficulties adapting to the system seemed to play a role [
      • Cook M.J.
      • O’Brien T.J.
      • Berkovic S.F.
      • Murphy M.
      • Morokoff A.
      • Fabinyi G.
      • et al.
      Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
      ].
      The main difficulty with this system is that it requires an invasive procedure that could have complications [
      • Cook M.J.
      • O’Brien T.J.
      • Berkovic S.F.
      • Murphy M.
      • Morokoff A.
      • Fabinyi G.
      • et al.
      Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
      ]. In one year after implantation, four patients (27%) had serious adverse effects, including device migration and infection. This study demonstrated for the first time that a seizure detection device predicted seizures better than chance [
      • Cook M.J.
      • O’Brien T.J.
      • Berkovic S.F.
      • Murphy M.
      • Morokoff A.
      • Fabinyi G.
      • et al.
      Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
      ].

      3.1.12 Skin temperature

      The ratio of mean temperature during sleep and awake periods could be related to seizures over the following days as suggested by recent work with wristband data in children with epilepsy. This work is in progress but if confirmed it suggests that skin temperature could also be analyzed for seizure detection [
      • Kim B.
      • Nogueira A.B.
      • Thome-Souza S.
      • Kapur K.
      • Klehm J.
      • Jackson M.
      • et al.
      Diurnal and nocturnal patterns of autonomic neurophysiological measurements are related to timing of seizures.
      ].

      3.1.13 Respiratory monitor

      Cardiac and respiratory abnormalities have been suggested as a cause for SUDEP [
      • Singh K.
      • Katz E.S.
      • Zarowski M.
      • Loddenkemper T.
      • Llewellyn N.
      • Manganaro S.
      • et al.
      Cardiopulmonary complications during pediatric seizures: a prelude to understanding SUDEP.
      ,
      • Johnston S.C.
      • Horn J.K.
      • Valente J.
      • Simon R.P.
      The role of hypoventilation in a sheep model of epileptic sudden death.
      ,
      • Bateman L.M.
      • Spitz M.
      • Seyal M.
      Ictal hypoventilation contributes to cardiac arrhythmia and SUDEP: report on two deaths in video-EEG-monitored patients.
      ]. In a study with 26 children (101 seizures) an elastic belt was used to monitor chest and abdominal excursion by respiratory inductance plethysmography [
      • Singh K.
      • Katz E.S.
      • Zarowski M.
      • Loddenkemper T.
      • Llewellyn N.
      • Manganaro S.
      • et al.
      Cardiopulmonary complications during pediatric seizures: a prelude to understanding SUDEP.
      ]. Of the seizures 39% were associated with ictal central apnea, 34% with ictal tachypnea, and 13% with ictal bradypnea. No patients had ictal obstructive sleep apnea [
      • Singh K.
      • Katz E.S.
      • Zarowski M.
      • Loddenkemper T.
      • Llewellyn N.
      • Manganaro S.
      • et al.
      Cardiopulmonary complications during pediatric seizures: a prelude to understanding SUDEP.
      ]. Ictal apnea was more frequent in temporal lobe seizures when compared to frontal seizures (OR 8.04, p = 0.0005). A thoracic band for detecting cardiac and respiratory changes could be coupled with a movement detection modality, potentially improving the overall sensitivity.

      3.2 Multimodal detection devices

      Seizure detection is more accurate if it combines more than one modality, as multimodal systems have shown increased sensitivity and lower FDR [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Kjaer T.W.
      • Sams T.
      • Sorensen H.B.
      Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data.
      ,
      • Milosevic M.
      • Van de Vel A.
      • Bonroy B.
      • Ceulemans B.
      • Lagae L.
      • VanRumste B.
      • et al.
      Automated detection of tonic-clonic seizures using 3D accelerometry and surface electromyography in pediatric patients.
      ,
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Terney D.
      • Sams T.
      • Sorensen H.B.
      Multi-modal intelligent seizure acquisition (MISA) system – a new approach towards seizure detection based on full body motion measures.
      ,
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Henriksen J.
      • Sams T.
      • Sorensen H.B.
      Seizure onset detection based on a uni- or multi-modal intelligent seizure acquisition (UISA/MISA) system.
      ].

      3.2.1 EDA and ACM

      The combination of EDA and ACM might improve detection of motor seizures and those with autonomic involvement. A system with EDA and ACM biosensors-placed on the ventral forearms of six patients with GTCS, during awake and sleep states – yielded 94% sensitivity and one false alarm per 24 h. The FDR was lower than for ACM alone [
      • Poh M.Z.
      • Loddenkemper T.
      • Reinsberger C.
      • Swenson N.C.
      • Goyal S.
      • Sabtala M.C.
      • et al.
      Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor.
      ]. The mean latency from clinical onset to detection was 31 s [
      • Poh M.Z.
      • Loddenkemper T.
      • Reinsberger C.
      • Swenson N.C.
      • Goyal S.
      • Sabtala M.C.
      • et al.
      Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor.
      ]. This emerging modality may benefit from more training in different settings, and preliminary results are promising.

      3.2.2 sEMG and ACM

      This combination yielded better results than each signal individually, and it also enhances detection of seizures with motor and autonomic system involvement. The modality was proposed because ACM seems to be more sensitive in detecting the clonic phase, and sEMG the tonic phase of seizures [
      • Milosevic M.
      • Van de Vel A.
      • Bonroy B.
      • Ceulemans B.
      • Lagae L.
      • VanRumste B.
      • et al.
      Automated detection of tonic-clonic seizures using 3D accelerometry and surface electromyography in pediatric patients.
      ]. In a study of children with tonic-clonic seizures, the combination of two ACM and two sEMG sensors achieved a sensitivity of 91%, with a FDR of 0.5/12 h and a latency of 10.5 s [
      • Milosevic M.
      • Van de Vel A.
      • Bonroy B.
      • Ceulemans B.
      • Lagae L.
      • VanRumste B.
      • et al.
      Automated detection of tonic-clonic seizures using 3D accelerometry and surface electromyography in pediatric patients.
      ]. The FDR was higher than previous studies on adults, which probably reflects the fact that children usually move more than adults at baseline [
      • Milosevic M.
      • Van de Vel A.
      • Bonroy B.
      • Ceulemans B.
      • Lagae L.
      • VanRumste B.
      • et al.
      Automated detection of tonic-clonic seizures using 3D accelerometry and surface electromyography in pediatric patients.
      ]. The ACM sensors yielded best results on the left wrist (non-dominant hand) and the right ankle [
      • Milosevic M.
      • Van de Vel A.
      • Bonroy B.
      • Ceulemans B.
      • Lagae L.
      • VanRumste B.
      • et al.
      Automated detection of tonic-clonic seizures using 3D accelerometry and surface electromyography in pediatric patients.
      ]. The use of isolated sEMG yielded a slightly lower sensitivity (82% vs 86%) when compared to isolated ACM, but FDR and latency were lower [
      • Milosevic M.
      • Van de Vel A.
      • Bonroy B.
      • Ceulemans B.
      • Lagae L.
      • VanRumste B.
      • et al.
      Automated detection of tonic-clonic seizures using 3D accelerometry and surface electromyography in pediatric patients.
      ]. sEMG alone detected tonic and hypermotor seizures, while ACM detected hypermotor, tonic, and clonic seizures [
      • Milosevic M.
      • Van de Vel A.
      • Bonroy B.
      • Ceulemans B.
      • Lagae L.
      • VanRumste B.
      • et al.
      Automated detection of tonic-clonic seizures using 3D accelerometry and surface electromyography in pediatric patients.
      ].

      3.2.3 Magnetometer and ACM

      The magnetometer is used as a seizure detection sensor because it characterizes movement in the horizontal plane, as seen in a tonic seizure [
      • Becq G.
      • Bonnet S.
      • Minotti L.
      • Antonakios M.
      • Guillemaud R.
      • Kahane P.
      Collection and exploratory analysis of attitude sensor data in an epilepsy monitoring unit.
      ]. Magnetometers measure body inclination using a 3D Earth's magnetic field sensor and track changes in orientation in 3D space [
      • Bonnet S.
      • Heliot R.
      A magnetometer-based approach for studying human movements.
      ]. Three sensor modules, each containing a tri-axial ACM and a tri-axial magnetometer, were placed in the head and both wrists of patients [
      • Becq G.
      • Bonnet S.
      • Minotti L.
      • Antonakios M.
      • Guillemaud R.
      • Kahane P.
      Collection and exploratory analysis of attitude sensor data in an epilepsy monitoring unit.
      ]. In a first phase, sensors discriminated tonic activity, clonic activity, hypermotor movements, and no movements [
      • Becq G.
      • Bonnet S.
      • Minotti L.
      • Antonakios M.
      • Guillemaud R.
      • Kahane P.
      Collection and exploratory analysis of attitude sensor data in an epilepsy monitoring unit.
      ]. Afterwards the system was tried in 86 patients in an epilepsy monitoring unit [
      • Becq G.
      • Kahane P.
      • Minotti L.
      • Bonnet S.
      • Guillemaud R.
      Classification of epileptic motor manifestations and detection of tonic-clonic seizures with acceleration norm entropy.
      ]. There was accurate classification of tonic seizures in 62% of cases and tonic-clonic in 90% of the cases [
      • Becq G.
      • Kahane P.
      • Minotti L.
      • Bonnet S.
      • Guillemaud R.
      Classification of epileptic motor manifestations and detection of tonic-clonic seizures with acceleration norm entropy.
      ]. Hypermotor seizures were classified as tonic-clonic seizures in 100%. The detectors had a 80% sensitivity with 95% specificity [
      • Becq G.
      • Kahane P.
      • Minotti L.
      • Bonnet S.
      • Guillemaud R.
      Classification of epileptic motor manifestations and detection of tonic-clonic seizures with acceleration norm entropy.
      ].

      3.2.4 Video, ACM and radar-induced activity recording (VARIA)

      Radars use wave transmission to identify motion caused, for example, by a change in the patient's position in bed. The combination of radar, video and ACM may increase the ability to detect seizures. This multimodal system considers the top 10% of events per night with the most abnormal movements, regardless of the number of seizures that occurred [
      • Van de Vel A.
      • Verhaert K.
      • Ceulemans B.
      Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures.
      ]. VARIA was tested in an 8-year-old patient with focal epilepsy with secondarily generalization. This system achieved a sensitivity of 56% and 20 false alarms per night [
      • Van de Vel A.
      • Verhaert K.
      • Ceulemans B.
      Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures.
      ]. The system detected some seizures that were missed by the staff [
      • Van de Vel A.
      • Verhaert K.
      • Ceulemans B.
      Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures.
      ]. Limitations of this system include numerous, easily removable bracelets [
      • Van de Vel A.
      • Verhaert K.
      • Ceulemans B.
      Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures.
      ].

      3.2.5 Multi-modal intelligent seizure acquisition (MISA) system

      The MISA system includes sEMG, magnetometers, ACM, and gyroscopes allowing full body movement description. Gyroscopes provide information on the rotation of each joint [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Kjaer T.W.
      • Sams T.
      • Sorensen H.B.
      Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data.
      ]. The system consists of a suit with 16 sensors, each one containing 3D accelerometer, 3D magnetometer, and 3D gyroscope, as well as 28 sEMG electrodes placed on 14 muscles [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Terney D.
      • Sams T.
      • Sorensen H.B.
      Multi-modal intelligent seizure acquisition (MISA) system – a new approach towards seizure detection based on full body motion measures.
      ]. The MISA system was first tried on three subjects that simulated tonic-clonic, versive asymmetric tonic, and myoclonic seizures [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Terney D.
      • Sams T.
      • Sorensen H.B.
      Multi-modal intelligent seizure acquisition (MISA) system – a new approach towards seizure detection based on full body motion measures.
      ]. The system achieved the best results when all modalities were used [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Terney D.
      • Sams T.
      • Sorensen H.B.
      Multi-modal intelligent seizure acquisition (MISA) system – a new approach towards seizure detection based on full body motion measures.
      ]. The non-specific system detected 98% of simulated seizures with one false positive per hour, and the subject specific system detected all seizures, with only one false positive in 4 h [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Terney D.
      • Sams T.
      • Sorensen H.B.
      Multi-modal intelligent seizure acquisition (MISA) system – a new approach towards seizure detection based on full body motion measures.
      ]. On another study the MISA system had the best results followed by the combination ACM and sEMG [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Kjaer T.W.
      • Sams T.
      • Sorensen H.B.
      Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data.
      ]. Some patients found the suit uncomfortable so the prototype is under modification, probably involving smaller and fewer electrodes [
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Kjaer T.W.
      • Sams T.
      • Sorensen H.B.
      Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data.
      ,
      • Conradsen I.
      • Beniczky S.
      • Wolf P.
      • Henriksen J.
      • Sams T.
      • Sorensen H.B.
      Seizure onset detection based on a uni- or multi-modal intelligent seizure acquisition (UISA/MISA) system.
      ].