Abstract

As one of the most challenging data analysis tasks in chronic brain diseases, epileptic seizure prediction has attracted extensive attention from many researchers. Seizure prediction, can greatly improve patients' quality of life in many ways, such as preventing accidents and reducing harm that may occur during epileptic seizures. This work aims to develop a general method for predicting seizures in specific patients through exploring the time-frequency correlation of features obtained from multi-channel EEG signals. We convert the original EEG signals into spectrograms that represent time-frequency characteristics by applying short-time Fourier transform (STFT) to the EEG signals. For the first time, we propose a dual self-attention residual network (RDANet) that combines a spectrum attention module integrating local features with global features, with a channel attention module mining the interdependence between channel mappings to achieve better forecasting performance. Our proposed approach achieved a sensitivity of 89.33%, a specificity of 93.02%, an AUC of 91.26% and an accuracy of 92.07% on 13 patients from the public CHB-MIT scalp EEG dataset. Our experiments show that different EEG signal prediction segment lengths are an important factor affecting prediction performance. Our proposed method is competitive and achieves good robustness without patient-specific engineering.

Highlights

  • A CCORDING to the International League Against Epilepsy (ILAE) report [1], epilepsy is defined as a group of neurological brain disorders due to excessive abnormal brain activities

  • We propose a patient-specific seizure prediction method by developing deep learning-based models to improve the performance of epileptic seizure prediction

  • We used a residual network to improve the performance of epileptic seizure prediction, and for the first time proposed a dual self-attention residual network (RDANet) to predict epileptic seizures

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Summary

Introduction

A CCORDING to the International League Against Epilepsy (ILAE) report [1], epilepsy is defined as a group of neurological brain disorders due to excessive abnormal brain activities. Epileptic seizures may cause loss of consciousness or perception and disorders of mood or other cognitive functions, even an increased risk of premature mortality [2]. There are differences in the frequency of seizures between different epileptic patients, ranging from less than one seizure per year to several seizures per day. It has been counted that approximate 50 million people around the world have epilepsy and up to 2 million new patients suffer from epilepsy every year [3]. The sub-project of National Key Research and Development Program of China 2016YFC1000307-10. The sub-project of National Key Research and Development Program of China 2016YFC1000307-10. (Corresponding authors: Jianbo Lu and Xu Ma.)

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