BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 1734 - 1743
• Main Authors: Xu, Zhendi, Wang, Tianlei, Cao, Jiuwen, Bao, Zihang, Jiang, Tiejia, Gao, Feng
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; BECT; Brain modeling; Child; Children; EEG; Electroencephalography; Epilepsy; Epilepsy - diagnosis; Feature extraction; Frequency-domain analysis; Humans; Long short-term memory; LSTM model; Nervous system; Neural networks; Neural Networks, Computer; Oversampling; Pediatrics; spike detection; Temporal Lobe; Time domain analysis; time domain EEG sequence features
• DOI: 10.1109/TNSRE.2021.3107142
• ISSN: 1534-4320; 1558-0210

The benign epilepsy with spinous waves in the central temporal region (BECT) is the one of the most common epileptic syndromes in children, that seriously threaten the nervous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram (EEG) spikes in the Rolandic area during the interictal period, that is an important basis to assist neurologists in BECT diagnosis. With this regard, the paper proposes a novel BECT spike detection algorithm based on time domain EEG sequence features and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The synthetic minority oversampling technique (SMOTE) is applied to address the spike imbalance issue in EEGs, and the bi-directional LSTM (BiLSTM) is trained for spike detection. The algorithm is evaluated using the EEG data of 15 BECT patients recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 85.75% precision, that generally outperforms several state-of-the-art spike detection methods.