Automatic Sleep Stage Classification by CNN-Transformer-LSTM using single-channel EEG signal
Duc Thien Pham, Roman Mouček
Abstract
Sleep stage classification plays a crucial role in diagnosing sleep disorders and understanding sleep physiology. In recent years, automated models based on machine learning and deep learning have gained attention for sleep stage classification. This paper uses the single-channel EEG signal to present an automatic sleep stage classification system using a combination of Convolutional Neural Network (CNN), Transformer, and Long Short-Term Memory (LSTM) models. Experimental evaluation of the ISRUC sleep datasets S1 and S3 demonstrates the effectiveness of the proposed model. It achieves accuracies of 80.37% and 82.40%, respectively, achieving competitive performance compared to state-of-the-art models.
Topics & Concepts
Computer scienceConvolutional neural networkElectroencephalographyArtificial intelligenceSleep StagesSleep (system call)TransformerDeep learningSpeech recognitionPattern recognition (psychology)Machine learningPolysomnographyPsychologyNeuroscienceEngineeringElectrical engineeringVoltageOperating systemEEG and Brain-Computer InterfacesSleep and Wakefulness ResearchSleep and Work-Related Fatigue