An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection

Jiazhi OU; Chang'an ZHAN; Feng YANG

Return

An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection

VernacularTitle:一维卷积神经网络的自编码癫痫发作异常检测模型
Author: Jiazhi OU ¹ ; Chang'an ZHAN ; Feng YANG
Author Information

1. 南方医科大学生物医学工程学院,广东广州 510515
Keywords: autoencoder; deep learning; seizure detection; anomaly detection; one-dimensional convolutional neural network
From: Journal of Southern Medical University 2024;44(9):1796-1804
CountryChina
Language:Chinese
Abstract: Objective We propose an autoencoder model based on a one-dimensional convolutional neural network(1DCNN)as the feature extraction network for efficient detection of epileptic EEG anomalies.Methods The local information of normal EEG signals was captured by utilizing the local feature extraction ability of 1DCNN for training of an autoencoder to learn the expression of normal EEG data in low dimensional feature space.With the difference between the input and output as the anomaly score,the threshold was determined by the optimal equilibrium point of the ROC curve,and the EEG signals exceeding the threshold were diagnosed as the seizure data.The performance of the 1DCNN-AE epilepsy detection model was evaluated using the publicly available CHB-MIT scalp EEG dataset and TUH scalp EEG dataset.Results The AUC of the 1DCNN-AE model reached 0.890 of CHB-MIT and 0.686 of TUH under the average level of patients,and the epilepsy detection rate reached 0.974 and 0.893,and these results were better than the latest epilepsy anomaly detection models LSTM-VAE and GRU-VAE.The 1DCNN model had a parameter quantity of 58.5M,which was at the same level with LSTM-VAE(47.4 M)and GRU-VAE(36.9 M)but with much smaller FLOPs(0.377 G)than LSTM-VAE(21.6 G)and GRU-VAE(16.2 G).Conclusion The autoencoder model based one-dimensional convolutional neural network can effectively detect abnormal EEG signals in epileptic seizure.