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KMID : 1144120230130040649
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Biomedical Engineering Letters
2023 Volume.13 No. 4 p.649 ~ p.658
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Development of generalizable automatic sleep staging using heart rate and movement based on large databases
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Lee Joon-Nyong
Kim Hee-Chan
Lee Yu-Jin
Lee Sa-Ram
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Abstract
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Purpose : With the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography.
Methods : A deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen’s κ as key metrics.
Results : The fine-tuned model showed accuracy of 76.6% with Cohen’s κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen’s κ of 0.673 in another external validation dataset.
Conclusion : These results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.
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KEYWORD
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Automatic sleep stage scoring, Home sleep monitoring, Polysomnography, Heart rate, Deep neural networks
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