BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms
BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms
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DOI：
10.1109/tnsre.2021.3107142
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发表时间：
2021-01-01
影响因子：
4.9
通讯作者：
Gao, Feng
中科院分区：
文献类型：
--
作者：
Xu, Zhendi;Wang, Tianlei;Gao, Feng
中央颞区棘波良性癫痫（BECT）是儿童最常见的癫痫综合征之一，严重威胁儿童神经系统发育。BECT最明显的特征是发作间期Rolandic区存在大量的脑电棘波，这是辅助神经科医生诊断BECT的重要依据。为此，提出了一种基于时域脑电序列特征和长短期记忆（LSTM）神经网络的BECT棘波检测算法。提取了三个能明显表征脑电棘波的时域序列特征，用于脑电的表征。采用合成少数过采样技术（SMOTE）解决脑电信号中的锋电位不平衡问题，并训练双向LSTM（BiLSTM）进行锋电位检测。使用15例BECT患者的EEG数据，浙江大学医学院附属儿童医院（CHZU）的算法进行了评估。实验结果表明，该算法的平均F1值为88.54%，灵敏度为92.04%，准确率为85.75%，优于现有的几种锋电位检测方法。
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.