Detection of sleep stages using EEG signal based on convolutional neural network
الموضوعات : journal of Artificial Intelligence in Electrical Engineering
1 - 1. Department of Electrical Engineering, Torbat-e Heydariyeh Branch, Islamic Azad University, Khorasan Razavi, Iran
الکلمات المفتاحية: EEG signal, deep learning, sleep stages, convolutional neural network.,
ملخص المقالة :
Diagnosing sleep and wakefulness is an important method in diagnosing sleep problems. This work is done by specialists based on the physical examination of biological signals such as EEG, EOG, ECG, EMG, etc. The deep learning method based on convolutional neural network is one of the newest and most important methods of analysis, separation, and diagnosis, which is expanding day by day. In this article, the deep learning-based convolutional neural network is used to extract features from the time-frequency domain of the EEG signal to classify sleep stages. Here, from the EEG signal, the time-frequency image of the signal is calculated based on the spectrogram. Then deep features are extracted using a convolutional neural network with Alexnet architecture with 8th-order fully connected layers. Finally, without changing the nature of the signal, sleep stages are detected with acceptable accuracy. Finally, by using the SVM classifier, sleep stages were classified with acceptable accuracy. An accuracy of 99.6% was obtained for the classification of sleep stages, which indicates the ability of the method to distinguish sleep stages.
