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

Chih-Fan KuoCheng-Yu TsaiWun-Hao ChengWen-Hua HsArnab MajumdarMarc StettlerKang-Yun LeeYi-Chun KuanPo-Hao FengChien-Hua TsengKuan-Yuan ChenJiunn-Horng KangHsin-Chien LeeCheng-Jung WuWen-Te Liu

Published in: Digital health (2023)

The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Keyphrases

heart rate variability
heart rate
machine learning
sleep quality
physical activity
risk assessment
artificial intelligence
deep learning