Version 1
: Received: 18 November 2020 / Approved: 19 November 2020 / Online: 19 November 2020 (11:10:07 CET)
How to cite:
Ni, H.; GUO, M.; Wang, Y. Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability. Preprints2020, 2020110506. https://doi.org/10.20944/preprints202011.0506.v1
Ni, H.; GUO, M.; Wang, Y. Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability. Preprints 2020, 2020110506. https://doi.org/10.20944/preprints202011.0506.v1
Ni, H.; GUO, M.; Wang, Y. Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability. Preprints2020, 2020110506. https://doi.org/10.20944/preprints202011.0506.v1
APA Style
Ni, H., GUO, M., & Wang, Y. (2020). Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability. Preprints. https://doi.org/10.20944/preprints202011.0506.v1
Chicago/Turabian Style
Ni, H., Mingzhe GUO and Ying Wang. 2020 "Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability" Preprints. https://doi.org/10.20944/preprints202011.0506.v1
Abstract
：Sleep stage on the whole night is not steady. Sleepers generally pass through three to five cycles. In each cycle, there are occur four typical sleep stages, such as wake stage (WS), light stage (LS), deep sleep (DS), rapid eye movement sleep stage (REM). According to the natural routine, in this paper, we investigate the stage transition and analyze the feature of stage transition using the local cluster Algorithm (LCA). Two-cycle sleep model (TCSM) is proposed to automatically classify sleep stages using over-night continuous heart rate variability (HRV) data. The generated model is based on the characteristics of the nested cycle's sleep stage distribution and the transition probabilities of sleep stages. Experiments were conducted using a public data set including 400 healthy subjects (female 239, male 161) and the model’s classification accuracy was evaluated for four sleep stages: WS, LS, DS, REM. The experimental results showed that based on the TCSM model, the segmentation classification of pure sleep is 5.2% higher than that of the traditional method, and the accuracy of segmentation classification is 11.2% higher than the traditional sleep staging accuracy. The experimental performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art methods. The study contributes to improve the quality of sleep monitoring in daily life using easy-to-wear HRV sensors.
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
