Reduced REM sleep: a potential biomarker for epilepsy – a retrospective case-control study


      • Determining a correct diagnosis in individuals with suspected epilepsy is challenging, if seizures and epileptic discharges are not recorded
      • Our aim was to assess whether the percentage of REM sleep (REM%) could discriminate between individuals with epilepsy and with differential diagnoses
      • We performed a retrospective, monocentric study with 128 individuals admitted to the EMU for diagnostic purposes
      • We found that REM% was significantly lower in individuals with the diagnosis of epilepsy and had a good predictive performance
      • Reduced REM% could be a supplementary biomarker for epilepsy in challenging diagnostic settings



      Establishing the diagnosis of epilepsy can be challenging if interictal epileptic discharges (IEDs) or seizures are undetectable. Many individuals with epilepsy experience sleep disturbances. A reduced percentage of REM sleep (REM%) has been observed following seizures. We aimed to assess differences of REM% in individuals with epilepsy in comparison with differential diagnoses.


      We performed a retrospective, monocentric, two-armed case-control study with 128 age-matched individuals who underwent ≥72 hours of continuous video-EEG monitoring at our epilepsy monitoring unit (EMU) for diagnostic evaluation. We assessed REM% on the first and last night of EMU admission. Logistic regressions models were used to evaluate the predictive value of REM%.


      We included 64 individuals diagnosed with epilepsy and 64 with a differential diagnosis. REM% in the epilepsy group was significantly lower [12.2% (±4.7) vs. 17.2% (±5.2), p<0.001]. We found no significant influence of sex, age, anti-seizure, or other medications. A REM%-based and an IED and seizure-based regression model were not significantly different [area under the curve (AUC) 0.791 (95% confidence interval (CI): 0.713-0.870) vs. 0.853 (95% CI: 0.788-0.919), p=0.23]. A combined model, based on IEDs, seizures, and REM%, was superior to the IED model alone [0.933 (0.891-0.975), p<0.01].


      Our study shows significantly reduced REM% in individuals with epilepsy. REM%-based models show a good predictive performance. REM% assessment could improve diagnostic accuracy – especially for challenging cases, e.g., when IEDs or seizures are absent and patient history and semiology appear ambiguous. REM% as a biomarker should be evaluated in prospective, multicentric trials.


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