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

Chih Fan Kuo, Cheng Yu Tsai, Wun Hao Cheng, Wen Hua Hs, Arnab Majumdar, Marc Stettler, Kang Yun Lee, Yi Chun Kuan, Po Hao Feng, Chien Hua Tseng, Kuan Yuan Chen, Jiunn Horng Kang, Hsin Chien Lee, Cheng Jung Wu, Wen Te Liu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalDigital Health
Volume9
DOIs
Publication statusPublished - 2023

Keywords

  • arousal
  • heart rate variability
  • InceptionTime model
  • Obstructive sleep apnea
  • the square roots of the means of the squares of successive differences between normal heartbeats (RMSSD)
  • the standard deviations of the time intervals between successive normal heartbeats (SDNN)

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles'. Together they form a unique fingerprint.

Cite this