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

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

doi:10.3389/fpsyt.2023.1104222

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

Front Psychiatry. 2023 Jun 6:14:1104222. doi: 10.3389/fpsyt.2023.1104222. eCollection 2023.

Authors

Xue Li¹, Chiaki Ono², Noriko Warita³, Tomoka Shoji^{1

3}, Takashi Nakagawa^{1

2}, Hitomi Usukura⁴, Zhiqian Yu⁴, Yuta Takahashi², Kei Ichiji⁵, Norihiro Sugita⁶, Natsuko Kobayashi², Saya Kikuchi², Ryoko Kimura¹, Yumiko Hamaie^{2

4}, Mizuki Hino⁴, Yasuto Kunii^{2

4}, Keiko Murakami³, Mami Ishikuro³, Taku Obara³, Tomohiro Nakamura⁷, Fuji Nagami⁸, Takako Takai⁷, Soichi Ogishima⁷, Junichi Sugawara⁹, Tetsuro Hoshiai¹⁰, Masatoshi Saito¹⁰, Gen Tamiya¹¹, Nobuo Fuse¹¹, Susumu Fujii¹², Masaharu Nakayama¹², Shinichi Kuriyama^{3

13}, Masayuki Yamamoto^{6

11}, Nobuo Yaegashi^{8

10}, Noriyasu Homma⁵, Hiroaki Tomita^{1

2

3

4}

Affiliations

¹ Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan.
² Department of Psychiatry, Tohoku University Hospital, Sendai, Japan.
³ Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.
⁴ Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan.
⁵ Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan.
⁶ Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai, Japan.
⁷ Department of Health Record Informatics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.
⁸ Department of Public Relations and Planning, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.
⁹ Department of Community Medical Supports, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.
¹⁰ Department of Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Japan.
¹¹ Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.
¹² Department of Disaster Medical Informatics, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan.
¹³ Department of Disaster Public Health, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan.

Abstract

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 sleep during pregnancy based on heart rate variability (HRV).

Methods: Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested.

Results and discussion: In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.

Keywords: deep learning; heart rate variability; machine learning; pregnant women; sleep condition; wake condition.

Copyright © 2023 Li, Ono, Warita, Shoji, Nakagawa, Usukura, Yu, Takahashi, Ichiji, Sugita, Kobayashi, Kikuchi, Kimura, Hamaie, Hino, Kunii, Murakami, Ishikuro, Obara, Nakamura, Nagami, Takai, Ogishima, Sugawara, Hoshiai, Saito, Tamiya, Fuse, Fujii, Nakayama, Kuriyama, Yamamoto, Yaegashi, Homma and Tomita.