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| Multi-Scale Deep Network for Automatic Sleep Staging |
| Bai Haoran, Zhang Wei#*, Lu Guanze |
| (Department of Control Science & Engineering, College of Electronics and Information Engineering,Tongji University, Shanghai 201804, China) |
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Abstract Sleep quality assessment and diagnosis highly depends on the doctors' effort and experience. It is quite labor intensive and time consuming for the doctor to inspect the long-term sleep monitoring records. The current automatic sleep staging mainly uses traditional machine learning and it highly relies on the features designed by experts. However, these features are usually incapable to capture the deep level features hidden in the measured data, and the behave not well for some staging such as N1. This paper proposed an automatic sleep staging algorithm based on multi-scale deep network. It used the deep network to automatically extract sleep signal features and used multi-scale analysis and discrimination criteria relating to the difficulty measure in the classification of different sleep stages. The classification results of stage W, N2, N3 and REM were selected and output in advance based on shallow layer features, and the fallible transition stage N1 entered a deeper network for further analysis. This policy improved the overall classification efficiency and especially the classification accuracy of the N1 stage. When extracting 197 sets of sample data for training and testing in the Sleep-EDFx data set and using only single-channel EEG signals, the average classification accuracy achieved 83%, and Kappa value was 0.749, which indicated that the constructed models were highly consistent. F1-score at stage N1 achieved 0.51. Compared with traditional machine learning algorithms and a variety of deep networks, the overall classification accuracy and accuracy of the N1 stage were improved. And at the same time, there was no apparent calculation increase. It is suitable for automatic real-time analysis.
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Received: 17 September 2020
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| About author:: Member, Chinese Society of Biomedical Engineering |
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