AS3-SAE: Automatic Sleep Stages Scoring Using Stacked Autoencoders

Mahtab  Vaezi; Mehdi Nasri

doi:10.18502/fbt.v10i4.13722

Mahtab Vaezi Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran
Mehdi Nasri Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

DOI: https://doi.org/10.18502/fbt.v10i4.13722

Keywords: Sleep Stages; Stacked Autoencoder; Single Channel Electroencephalogram; Deep Learning; Electrocardiogram.

Abstract

Purpose: Sleep is a subconscious state, and the brain is active during it. Automatic classification of sleep stages can help identify various diseases. In recent years, automatic sleep monitoring using deep learning networks has attracted the attention of researchers.

Materials and Methods: In this paper, a deep learning type neural network called Stacked Autoencoders (SAEs) is used for automatically classifying sleep stages. SAEs are a kind of neural network with encoder and decoder blocks. The function of these networks is similar to the human brain and is capable of automatically processing signals; also SAEs are robust to noise. To prove the efficiency of this network, in addition to examining the effect of various biological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) on the performance of sleep stage classification, Sleep Heart Health Study (SHHS) and ISRUC standard databases have been used, which include night recordings of 30 and 10 healthy humans, respectively.

Results: The accuracy of classifying 2 to 6 classes by SHHS database are 0.995, 0.983, 0.9780, 0.9688, 0.961, and on ISRUC database accuracies are 0.996, 0.994, 0.9511, and 0.9431. Moreover, the proposed network can classify wake, deep sleep, and light sleep using the ECG signal (acc = 0.75, kappa = 0.69).

Conclusion: In the review of the results, it is concluded that sleep stages classification based on EEG signal has better results, still acquisition of ECG signal and its acceptable results can be a good alternative to use. In addition to its high ability of the proposed method to detect sleep stages, this network is robust to noise, which is very necessary and important for the clinical processing of sleep signals.