Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 30, Issue -, Pages 2641-2650Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2022.3204533
Keywords
Electroencephalography; Recording; Epilepsy; Databases; Training; Discrete wavelet transforms; Collaboration; Electroencephalogram; seizure detection; graph-regularized non-negative matrix factorization; kernel method; robust probabilistic collaborative representation
Categories
Funding
- National Natural Science Foundation of China [61701279, 61902215, 62172254, 62172253]
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An automatic seizure detection method based on kernel-based robust probabilistic collaborative representation (ProCRC) and graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The method achieves high sensitivity and specificity by preprocessing and dimension reduction of EEG signals, and using robust ProCRC for representing test samples.
Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development. An automatic detection of epileptic seizure method based on kernel-based robust probabilistic collaborative representation (ProCRC) combined with graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The raw EEG signals are pre-processed through the wavelet transform to obtain time-frequency distribution of EEG signals as preliminary feature information and GNMF is further employed for dimension reduction, retaining and enhancing the productive feature information of EEG signals. Then, the test sample is represented using robust ProCRC that can decide whether the testing sample belongs to each class (seizure or non-seizure) by jointly maximizing the likelihood. In addition, the kernel trick is applied to improve the separability of non-linear high dimensional EEG signals in robust ProCRC. Finally, post-processing techniques are introduced to generate more accurate and reliable results. The average epoch-based sensitivity of 96.48%, event-based sensitivity of 93.65% and specificity of 98.55% are acquired in this method, which is evaluated on the public Freiburg EEG database.
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