TY - JOUR
T1 - Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women
AU - Li, Xue
AU - Ono, Chiaki
AU - Warita, Noriko
AU - Shoji, Tomoka
AU - Nakagawa, Takashi
AU - Usukura, Hitomi
AU - Yu, Zhiqian
AU - Takahashi, Yuta
AU - Ichiji, Kei
AU - Sugita, Norihiro
AU - Kobayashi, Natsuko
AU - Kikuchi, Saya
AU - Kunii, Yasuto
AU - Murakami, Keiko
AU - Ishikuro, Mami
AU - Obara, Taku
AU - Nakamura, Tomohiro
AU - Nagami, Fuji
AU - Takai, Takako
AU - Ogishima, Soichi
AU - Sugawara, Junichi
AU - Hoshiai, Tetsuro
AU - Saito, Masatoshi
AU - Tamiya, Gen
AU - Fuse, Nobuo
AU - Kuriyama, Shinichi
AU - Yamamoto, Masayuki
AU - Yaegashi, Nobuo
AU - Homma, Noriyasu
AU - Tomita, Hiroaki
N1 - Funding Information:
We are grateful to Drs. Ichiro Tsuji, Takako Takai-Igarashi, Osamu Tanabe, Tadashi Ishii, Kiyoshi Ito, Eiichi N. Kodama, Yasuyuki Taki, Masao Nagasaki, Ritsuko Shimizu, Akito Tsuboi, Kichiya Suzuki, Hiroshi Tanaka, Hiroshi Kawame, Hiroaki Hashizume, Sadayoshi Ito, and all faculty and staff of the Tohoku University Tohoku Medical Megabank Organization (http://www.megabank.tohoku.ac.jp/english/a191201/) for establishing the three-generation cohort based on the add-on cohort.
Funding Information:
This research was supported by a grant from the Strategic Research Program for Brain Sciences from the Japan Agency for Medical Research and Development (AMED) under (Grant Number JP20dm0107099), the Tohoku Medical Megabank Project from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan and AMED under (Grant Numbers JP20km0105001 and JP20km0105002), and Tohoku University Advanced Research Center for Innovations in Next-Generation Medicine. We are grateful to the project participants for supporting this study.
Publisher Copyright:
Copyright © 2022 Li, Ono, Warita, Shoji, Nakagawa, Usukura, Yu, Takahashi, Ichiji, Sugita, Kobayashi, Kikuchi, Kunii, Murakami, Ishikuro, Obara, Nakamura, Nagami, Takai, Ogishima, Sugawara, Hoshiai, Saito, Tamiya, Fuse, Kuriyama, Yamamoto, Yaegashi, Homma and Tomita.
PY - 2022/1/27
Y1 - 2022/1/27
N2 - 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.
AB - 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.
KW - autonomic system
KW - emotion
KW - ensemble learning
KW - gradient boosting trees
KW - heart rate variability
KW - machine learning
KW - pregnancy
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85124523257&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124523257&partnerID=8YFLogxK
U2 - 10.3389/fpsyt.2021.799029
DO - 10.3389/fpsyt.2021.799029
M3 - Article
AN - SCOPUS:85124523257
SN - 1664-0640
VL - 12
JO - Frontiers in Psychiatry
JF - Frontiers in Psychiatry
M1 - 799029
ER -