Automatic Sleep Stage Classification by CNN Transformer LSTM using Single Channel EEG

Apr 14, 2026

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This paper proposes an automatic sleep stage classification system using single-channel EEG signals. The method combines Convolutional Neural Networks (CNN), Transformer, and Long Short-Term Memory (LSTM) models to capture local and global EEG features under the AASM scoring rules. Experiments on the ISRUC Sleep dataset S1 and S3 show accuracies of 80.37% and 82.40%, demonstrating competitive performance against state-of-the-art approaches.

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Automatic Sleep Stage Classification by CNN Transformer LSTM using Single Channel EEG

Apr 14, 2026

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