Key result
The Random Forest machine learning algorithm predicted four different emotions based on heart rate variability indicators with an area under the receiver operating characteristic curve of 0.70.
Why the study?
The extent to which different emotions of pregnant women can be predicted based on heart rate-relevant autonomic nervous system indicators using machine learning algorithms was unexplored.
Can machine learning algorithms predict emotions in pregnant women using heart rate-relevant autonomic system indicators?
Observational (n=85)
No
Can machine learning algorithms predict emotions in pregnant women using heart rate-relevant autonomic system indicators?
Machine learning models, particularly Random Forest and Gradient Boosting Trees, can modestly predict emotions in pregnant women using heart rate variability indicators.
Modest accuracy cautions against clinical use; leaves open whether refined HRV-ML models can aid emotion monitoring in pregnancy.
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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Li et al. (2022) conducted an observational in Pregnancy (emotion prediction) (n=85). Random Forest machine learning algorithm using HRV vs. Other machine learning algorithms was evaluated on Area under the receiver operating characteristic curve (AUC-ROC) for predicting four emotions. The Random Forest machine learning algorithm predicted four different emotions based on heart rate variability indicators with an area under the receiver operating characteristic curve of 0.70.
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