Epileptic Seizure Prediction Based on Multiresolution Convolutional Neural Networks

Abstract Epilepsy withholds patients' control of their body or consciousness and puts them at risk in the course of their daily life. This paper pursues the development of a smart neurocomputational technology to alert epileptic patients wearing EEG sensors of an impending seizure. An innovative approach to epileptic seizure prediction is proposed to improve prediction accuracy and reduce false alarm rate in comparison with state of art benchmarks. Maximal Overlap Discrete Wavelet Transform is used to decompose EEG signals into different frequency resolutions, and a Multiresolution Convolutional Neural Network is designed to extract discriminative features from each frequency band. The algorithm automatically generates patient-specific features to best classify preictal and interictal segments of the subject. The method can be applied to any patient case from any dataset without the need for a handcrafted feature extraction procedure. The proposed approach was tested with two popular epilepsy patient datasets. It achieved a sensitivity of 82% and a false prediction rate of 0.058 with the Children’s Hospital Boston-MIT scalp EEG dataset, and a sensitivity of 85% and a false prediction rate of 0.19 with the American Epilepsy Society Seizure Prediction Challenge dataset. This technology provides a personalized solution for the patient that has improved sensitivity and specificity, yet, because of the algorithm's intrinsic ability for generalization, it emancipates from the reliance on epileptologists' expertise to tune a wearable technological aid, which ultimately will help deploy it broadly, including in medically-underserved locations in the globe.