Classification of sleep stages from EEG, EOG and EMG signals by SSNet

TL;DR

This paper introduces SSNet, an end-to-end deep learning model combining CNN and LSTM to classify sleep stages from EEG, EOG, and EMG signals, achieving high accuracy on public datasets.

Contribution

The study presents a novel deep learning architecture, SSNet, that effectively fuses features from multiple biosignals for sleep stage classification, outperforming existing methods.

Findings

Achieved over 96% accuracy in sleep stage classification

Outperformed state-of-the-art techniques in benchmark datasets

Demonstrated effective multi-signal feature fusion

Abstract

Classification of sleep stages plays an essential role in diagnosing sleep-related diseases including Sleep Disorder Breathing (SDB) disease. In this study, we propose an end-to-end deep learning architecture, named SSNet, which comprises of two deep learning networks based on Convolutional Neuron Networks (CNN) and Long Short Term Memory (LSTM). Both deep learning networks extract features from the combination of Electrooculogram (EOG), Electroencephalogram (EEG), and Electromyogram (EMG) signals, as each signal has distinct features that help in the classification of sleep stages. The features produced by the two-deep learning networks are concatenated to pass to the fully connected layer for the classification. The performance of our proposed model is evaluated by using two public datasets Sleep-EDF Expanded dataset and ISRUC-Sleep dataset. The accuracy and Kappa coefficient are 96.36% and 93.40% respectively, for classifying three classes of sleep stages using Sleep-EDF Expanded dataset. Whereas, the accuracy and Kappa coefficient are 96.57% and 83.05% respectively for five classes of sleep stages using Sleep-EDF Expanded dataset. Our model achieves the best performance in classifying sleep stages when compared with the state-of-the-art techniques.