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

INTRODUCTION: Perinatal 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 s...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Xue, Ono, Chiaki, Warita, Noriko, Shoji, Tomoka, Nakagawa, Takashi, Usukura, Hitomi, Yu, Zhiqian, Takahashi, Yuta, Ichiji, Kei, Sugita, Norihiro, Kobayashi, Natsuko, Kikuchi, Saya, Kimura, Ryoko, Hamaie, Yumiko, Hino, Mizuki, Kunii, Yasuto, Murakami, Keiko, Ishikuro, Mami, Obara, Taku, Nakamura, Tomohiro, Nagami, Fuji, Takai, Takako, Ogishima, Soichi, Sugawara, Junichi, Hoshiai, Tetsuro, Saito, Masatoshi, Tamiya, Gen, Fuse, Nobuo, Fujii, Susumu, Nakayama, Masaharu, Kuriyama, Shinichi, Yamamoto, Masayuki, Yaegashi, Nobuo, Homma, Noriyasu, Tomita, Hiroaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Psychiatry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322181/
https://www.ncbi.nlm.nih.gov/pubmed/37415686
http://dx.doi.org/10.3389/fpsyt.2023.1104222

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322181/
https://www.ncbi.nlm.nih.gov/pubmed/37415686
http://dx.doi.org/10.3389/fpsyt.2023.1104222

Ejemplares similares