Data-Driven Separation and Estimation of Atrial Dynamics in Very High-Dimensional Electrocardiograms from Epilepsy Patients

Abstract

Across biomedical areas, there is a significant unmet need for multimodal biomarkers that can improve prediction of abnormal events such as seizures, heart and asthma attacks or stroke. These markers may be multimodal and may include electrophysiological measures estimated from noninvasive, routinely collected clinical data, such as electroencephalograms (EEG) and electrocardiograms (ECG). In epilepsy, seizure detection and prediction from noninvasive data remains a difficult problem in need of novel approaches and markers. The inherent noise in high-dimensional EEG signals and artifact contamination often severely impacts the sensitivity and specificity of otherwise promising biomarkers. Long-term epilepsy clinical studies typically collect continuous ECG from which additional features may be estimated and combined with EEG measures to improve sensitivity to ictogenesis and seizure specificity. Prior work has focused on ventricular activity and features of the QRS complex, but atrial activity may also be modulated by seizure evolution. Given the high dimension of the ECG collected continuously over several days, an entirely data-driven approach is proposed, based on which ECG signals may be separated into ventricular and atrial contributions and studied separately. The relationship of atrial dynamics to seizure occurrence is assessed in a small number of pediatric epilepsy patients with high-dimensional ECG.