Automatic Sleep Stage Classification Using Temporal Convolutional Neural Network and New Data Augmentation Technique from Raw Single-Channel EEG. (June 2021)

• Record Type: Journal Article
• Authors: Khalili, Ebrahim; Mohammadzadeh Asl, Babak
• Is Part Of: Computer methods and programs in biomedicine. Volume 204(2021)
• Publication Date: 2021-06
• Subjects: Sleep stage classification -- Single channel EEG -- Deep learning -- Temporal Convolutional Neural Network -- Data augmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106063
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new framework for automatic sleep stage classification from single-channel EEG. Using a deep learning algorithm including TCNN and new data augmentation technique. Evaluating our model using two public sleep datasets, Sleep-EDF-2013 and 2018. The best total accuracy and kappa score compared to the state-of-the-art methods. Abstract: Background and objective: This paper presents a new framework for automatic classification of sleep stages using a deep learning algorithm from single-channel EEG signals. Each segmented EEG signal appended with its label of stages is fed into a deep learning model to create an automatic sleep stage classification. This is one of the most important problems that is critical to the realization of monitoring patients with sleep disorder. Methods: In the present study, a neural network architecture is introduced utilizing Convolutional Neural Networks (CNNs) to extract features, followed by Temporal Convolutional Neural Network to extract the temporal features from the extracted features vector of CNN. Finally, the performance of our model is improved by a Conditional Random Field layer. We also employed a new data augmentation technique to enhance the CNNs training which has auxiliary effects. Results: We evaluated our model by two different single-channel EEG signals (i.e., Fpz-Cz and Pz-Oz EEG channels) from two public sleep datasets, named Sleep-EDF-2013 and Sleep-EDF-2018. The evaluation results on both datasets showed that our model obtains the best total accuracy and kappa score (EDF-2013: 85.39%- 0.80, EDF-2018: 82.46%- 0.76) compared to the state-of-the-art methods. Conclusions: This study will possibly allow us to have a wearable sleep monitoring system with a single-channel EEG. Also, unlike hand-crafted features methods, our model finds its own patterns through training epochs, and therefore, it may minimize engineering bias.