Auto-SleepNet: A CPU-Driven Deep Learning Approach for Sleep Stage Classification using Single-Lead Electroencephalography Signals

A large group of people, nowadays, has been suffering from chronic sleep disorders and diseases, resulting in wide attention on sleep quality assessment. Conventional sleep-staging networks frequently consider multiple channel inputs, hindering the feasibility of the network to single-channel input or other sensor data input. In this paper, we proposed an Auto-SleepNet: a CPU-driven and end-to-end deep learning network for sleep stage classification using single-lead electroencephalogram (EEG) signals. The network is composed of a tailored Auto-Encoder for feature extraction and correction, and an LSTM network for temporal-signal classification. Compared with multi-lead connections, our design renders a higher accuracy in comparison to state-of-the-art, provides a meaningful reference for simplifying the hardware requirements of the EEG measurement device, and simultaneously lowers the computational loads significantly. We used the Per-Class precision (PR), Recall (RE), Per-Class F1 Score, overall accuracy, confusion matrix, and Cohen’s kappa coefficient (κ) to evaluate the performance. The overall accuracy, RE, and Cohen’s Kappa of our model are 95.7%, 95.19% and 0.91, respectively. Compared to state-of-the-art methods mentioned in the paper, Auto-SleepNet outperforms single-channel methods by 13.97%, and multiple-channel methods by 15.97% on average. Furthermore, it is not compulsory to use a GPU to train our Auto-SleepNet. Experiments show that our model can converge in 15.6 minutes using a CPU only. The results highlight the practicability of the network to sleep stage classification problems.