Frontiers in Psychiatry (Jan 2022)

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

  • Xue Li,
  • Chiaki Ono,
  • Noriko Warita,
  • Tomoka Shoji,
  • Tomoka Shoji,
  • Takashi Nakagawa,
  • Takashi Nakagawa,
  • Hitomi Usukura,
  • Zhiqian Yu,
  • Yuta Takahashi,
  • Kei Ichiji,
  • Norihiro Sugita,
  • Natsuko Kobayashi,
  • Saya Kikuchi,
  • Yasuto Kunii,
  • 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,
  • Shinichi Kuriyama,
  • Masayuki Yamamoto,
  • Masayuki Yamamoto,
  • Nobuo Yaegashi,
  • Nobuo Yaegashi,
  • Noriyasu Homma,
  • Hiroaki Tomita,
  • Hiroaki Tomita,
  • Hiroaki Tomita,
  • Hiroaki Tomita

DOI
https://doi.org/10.3389/fpsyt.2021.799029
Journal volume & issue
Vol. 12

Abstract

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In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including “happy,” as a positive emotion and “anxiety,” “sad,” “frustrated,” as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.

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