Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles

Author:

Kuo Chih-Fan123,Tsai Cheng-Yu45,Cheng Wun-Hao67,Hs Wen-Hua6,Majumdar Arnab4,Stettler Marc4,Lee Kang-Yun58,Kuan Yi-Chun9101112,Feng Po-Hao58,Tseng Chien-Hua58,Chen Kuan-Yuan5,Kang Jiunn-Horng1314,Lee Hsin-Chien15,Wu Cheng-Jung16,Liu Wen-Te56913ORCID

Affiliation:

1. School of Medicine, China Medical University, Taichung City, Taichung, Taiwan

2. Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan

3. Department of Medical Education, Chung Shan Medical University Hospital, Taichung, Taiwan

4. Department of Civil and Environmental Engineering, Imperial College London, London, UK

5. Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan

6. School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan

7. Respiratory Therapy, Division of Pulmonary Medicine, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan

8. Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan

9. Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan

10. Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan

11. Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

12. Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan

13. Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan

14. Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan

15. Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan

16. Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan

Abstract

Objective Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters—namely heart rate variability, oxygen saturation, and body profiles—to predict arousal occurrence. Methods Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Funder

Taiwan’s National Science and Technology Council

Publisher

SAGE Publications

Subject

Health Information Management,Computer Science Applications,Health Informatics,Health Policy

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