Automatic detection for epileptic seizure using graph-regularized nonnegative matrix factorization and Bayesian linear discriminate analysis

Epilepsy is a neurological disorder characterized by excessive neuronal discharge which results in many problems in terms of behavior, state of mind, consciousness, and can threaten the lives of patients. An automatic epileptic seizure detection method with graph-regularized non-negative matrix factorization (GNMF) and Bayesian linear discrimi-nate analysis (BLDA) is presented in this paper. First, discrete wavelet decomposition is applied to analyze raw electroencephalogram (EEG) signals, and the normalization based on differential operator is used to guarantee the nonnegative constraint and reinforce the distinction between seizure and non-seizure signals. Then, GNMF is employed to dimensionality reduction and feature extraction for EEG data, which could capture a parts-based representation of samples and obtain more discriminative features. The EEG features are calculated and entered into the BLDA classifier for categorized results. The public Freiburg EEG database is used to evaluate the performance of the proposed seizure detection method. The results showed event-based sensitivity of 95.24%, epoch-based sen-sitivity of 93.20%, and a false-alarm rate of 0.5/h. These results demonstrate the potential clinical value of this method for automatic seizure detection. (c) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Automated summary

The paper presents an automatic epileptic seizure detection method based on GNMF and BLDA, which effectively enhances the classification accuracy of EEG data through discrete wavelet decomposition and feature extraction. The method demonstrates high sensitivity and low false alarm rate in testing.