Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG

Abstract

A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring. To classify the sleep stage from half-minute EEG, called an epoch, sleep experts investigate sleep-related events and consider the transition rules between the found events. Similarly, IITNet extracts representative features at a sub-epoch level by a residual neural network and captures intra- and inter-epoch temporal contexts from the sequence of the features via bidirectional LSTM. The performance was investigated for three datasets as the sequence length (L) increased from one to ten. IITNet achieved the comparable performance with other state-of-the-art results. The best accuracy, MF1, and Cohen's kappa ($\kappa$) were 83.9%, 77.6%, 0.78 for SleepEDF (L=10), 86.5%, 80.7%, 0.80 for MASS (L=9), and 86.7%, 79.8%, 0.81 for SHHS (L=10), respectively. Even though using four epochs, the performance was still comparable. Compared to using a single epoch, on average, accuracy and MF1 increased by 2.48%p and 4.90%p and F1 of N1, N2, and REM increased by 16.1%p, 1.50%p, and 6.42%p, respectively. Above four epochs, the performance improvement was not significant. The results support that considering the latest two-minute raw single-channel EEG can be a reasonable choice for sleep scoring via deep neural networks with efficiency and reliability. Furthermore, the experiments with the baselines showed that introducing intra-epoch temporal context learning with a deep residual network contributes to the improvement in the overall performance and has the positive synergy effect with the inter-epoch temporal context learning.

arXiv commentFirst three authors contributed equally to this work; Accepted manuscript for Biomedical Signal Processing and Control (BSPC); 12 pages, 6 figures;

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
• Hierarchical Question-Image Co-Attention for Visual Question Answering
• SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
• A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
• Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
• Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy
• Mixed Neural Network Approach for Temporal Sleep Stage Classification
• SLEEPNET: Automated Sleep Staging System via Deep Learning

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