A sparse multiscale nonlinear autoregressive model for seizure prediction

• Published in: Journal of neural engineering Vol. 18; no. 2; p. 26012
• Main Authors: Yu, Pen-Ning, Liu, Charles Y, Heck, Christianne N, Berger, Theodore W, Song, Dong
• Format: Journal Article
• Language: English
• Published: England 01.04.2021
• Subjects: Laguerre expansion; epilepsy; seizure prediction; multi-scale model; autoregressive model; nonlinear dynamical model; Volterra model
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2552/abdd43

Accurate seizure prediction is highly desirable for medical interventions such as responsive electrical stimulation. We aim to develop a classification model that can predict seizures by identifying preictal states, i.e. the precursor of a seizure, based on multi-channel intracranial electroencephalography (iEEG) signals. A two-level sparse multiscale classification model was developed to classify interictal and preictal states from iEEG data. In the first level, short time-scale linear dynamical features were extracted as autoregressive (AR) model coefficients; arbitrary (usually long) time-scale linear and nonlinear dynamical features were extracted as Laguerre-Volterra AR model coefficients; root-mean-square error of model prediction was used as a feature representing model unpredictability. In the second level, all features were fed into a sparse classifier to discriminate the iEEG data between interictal and preictal states. . The two-level model can accurately classify seizure states using iEEG data recorded from ten canine and human subjects. Adding arbitrary (usually long) time-scale and nonlinear features significantly improves model performance compared with the conventional AR modeling approach. There is a high degree of variability in the types of features contributing to seizure prediction across different subjects. . This study suggests that seizure generation may involve distinct linear/nonlinear dynamical processes caused by different underlying neurobiological mechanisms. It is necessary to build patient-specific classification models with a wide range of dynamical features.