Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Sudden unexpected death in epilepsy: respiratory vs. cardiac contributions

Sudden unexpected death in epilepsy (SUDEP) poses a significant risk to life expectancy for individuals with epilepsy. Mechanistic insight, while incomplete, has advanced through clinical observational studies and animal models. Yet we lack preventative therapies, which will depend on understanding SUDEP mechanisms. Recurrent convulsive seizures are the major SUDEP risk factor. Cardiorespiratory dysfunction precedes SUDEP, but whether cardiac arrhythmias are major proximate culprits for SUDEP remains to be determined. Here, we highlight recent data from mouse models and clinical studies that provide increasing support for respiratory depression and decreasing evidence for tachyarrhythmia-induced SUDEP. Further, we review data from genetic and chemoconvulsant mouse models that have enabled a deeper understanding for how seizures initiated in the central nervous system propagate to the autonomic nervous system and drive seizure-induced respiratory depression and subsequent SUDEP, rather than supporting a proximate cardiac arrhythmia cause. Ongoing research will continue to identify predictive SUDEP biomarkers, improve animal models, and translate basic research into precision medicine approaches. Identifying and understanding the brainstem circuits vulnerable in seizure-induced apnoea will enable therapeutic interventions to enhance the quality of life and life expectancy for individuals with epilepsy.

Electroencephalography characteristics related to risk of sudden unexpected death in epilepsy in patients with Dravet syndrome

Objective

To investigate the quantitative electroencephalography (EEG) features associated with a high risk of sudden unexpected death in epilepsy (SUDEP) in patients with Dravet syndrome (DS).

Methods

Patients with DS and healthy controls (HCs) who underwent EEG were included in the study. EEG signals were recorded using a 21 channel digital EEG system, and pre-processed data were analyzed to identify quantitative EEG features associated with a high SUDEP risk. To assess the risk of SUDEP, SUDEP-7 scores were used.

Results

A total of 64 patients with DS [38 males and 26 females, aged: 128.51 ± 75.50 months (range: 23-380 months)], and 13 HCs [7 males and 6 females, aged: 95.46 ± 86.48 months (range: 13-263 months)] were included. For the absolute band power, the theta power was significantly higher in the high-SUDEP group than in the low-SUDEP group in the central brain region. For the relative band power, the theta power was also significantly higher in the high-SUDEP group than in the low-SUDEP group in the central and occipital brain regions. The alpha power was significantly lower in the high-SUDEP group than in the low-SUDEP group in the central and parietal brain regions.

Conclusion

Patients with high SUDEP-7 scores have different EEG features from those with low SUDEP-7 scores, suggesting that EEG may be used as a biomarker of SUDEP in DS.

Significance

Early intervention in patients with DS at a high risk of SUDEP can reduce mortality and morbidity. Patients with high theta band powers warrant high-level supervision.

Toward automated prediction of sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.