Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, 250358, China.
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.
Comput Biol Med. 2020 Sep;124:103919. doi: 10.1016/j.compbiomed.2020.103919. Epub 2020 Jul 18.
Automatic seizure detection technology not only reduces workloads of neurologists for epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A novel seizure detection method based on the deep bidirectional long short-term memory (Bi-LSTM) network is proposed in this paper. To preserve the non-stationary nature of EEG signals while decreasing the computational burden, the local mean decomposition (LMD) and statistical feature extraction procedures are introduced. The deep architecture is then designed by combining two independent LSTM networks with the opposite propagation directions: one transmits information from the front to the back, and another from the back to the front. Thus the deep model can take advantage of the information both before and after the currently analyzing moment to jointly determine the output state. A mean sensitivity of 93.61% and a mean specificity of 91.85% were achieved on a long-term scalp EEG database. The comparisons with other published methods based on either traditional machine learning models or convolutional neural networks demonstrated the improved performance for seizure detection.
自动癫痫发作检测技术不仅减轻了神经科医生进行癫痫诊断的工作量，而且对癫痫患者的治疗也具有重要意义。本文提出了一种基于深度双向长短期记忆（Bi-LSTM）网络的新型癫痫发作检测方法。为了在降低计算负担的同时保留 EEG 信号的非平稳性，引入了局部均值分解（LMD）和统计特征提取过程。然后，通过结合两个具有相反传播方向的独立 LSTM 网络来设计深度架构：一个从前向后传输信息，另一个从后向前传输信息。因此，深度模型可以利用当前分析时刻前后的信息来共同确定输出状态。在长期头皮 EEG 数据库上，获得了 93.61%的平均灵敏度和 91.85%的平均特异性。与其他基于传统机器学习模型或卷积神经网络的已发表方法的比较表明，该方法在癫痫发作检测方面具有更好的性能。
