Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability

Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability

IntroductionPerinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep–wake conditions and differentiating between the wake conditions before and after sle...

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Main Authors: Xue Li, Chiaki Ono, Noriko Warita, Tomoka Shoji, Takashi Nakagawa, Hitomi Usukura, Zhiqian Yu, Yuta Takahashi, Kei Ichiji, Norihiro Sugita, Natsuko Kobayashi, Saya Kikuchi, Ryoko Kimura, Yumiko Hamaie, Mizuki Hino, Yasuto Kunii, Keiko Murakami, Mami Ishikuro, Taku Obara, Tomohiro Nakamura, Fuji Nagami, Takako Takai, Soichi Ogishima, Junichi Sugawara, Tetsuro Hoshiai, Masatoshi Saito, Gen Tamiya, Nobuo Fuse, Susumu Fujii, Masaharu Nakayama, Shinichi Kuriyama, Masayuki Yamamoto, Nobuo Yaegashi, Noriyasu Homma, Hiroaki Tomita
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-06-01
Series:Frontiers in Psychiatry
Subjects:
deep learning
heart rate variability
machine learning
pregnant women
sleep condition
wake condition
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1104222/full

Internet

https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1104222/full

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