A sparse multiscale nonlinear autoregressive model for seizure prediction

Abstract

Objectives. 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. Approach. 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. Main results. 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. Significance. 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.

Epileptic seizure prediction using successive variational mode decomposition and transformers deep learning network

“…More details were described in reference (Brinkmann et al, 2016 ). In this dataset, 1 h before seizure with a 5-min horizon (i.e., 66–5 min before seizure onset) was chosen as preictal phase (Brinkmann et al, 2016 ; Assi et al, 2017a ; Gagliano et al, 2019 ; Nejedly et al, 2019 ; Yu et al, 2021 ). Each consecutive interictal sequence lasted for 1 h, which were randomly chosen from iEEG recordings more than 1 week (dogs) and 4 h (patients) before or after any seizure.…”

Epileptic Seizure Prediction Using Convolutional Neural Networks and Fusion Features on Scalp EEG Signals

“…Mirowski et al [9] compared L1-regularized logistic regression, convolutional networks, and support vector machines based on aggregated features, such as cross-correlation, nonlinear interdependence. Yu et al [10] developed a two-level sparse multiscale classification model to classify interictal and preictal states from iEEG data. Prathaban et al [11] From the existing studies, it can be observed that convolutional neural network has been widely used in epilepsy prediction approaches, which achieved great results [5], [6],…”