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Mental Health Screening Using the Heart Rate Variability and Frontal Electroencephalography Features: A Machine

Je-Yeon Yun^1,2, Goomin Kwon^3,4, Miseon Shim⁵

¹Seoul National University Hospital, Seoul, KR.

JMIR Mental Health

|February 19, 2025

Summary

This study combined heart rate variability (HRV) and electroencephalography (EEG) to accurately screen for psychiatric disorders (PT) in individuals. Machine learning models effectively identified PT versus healthy controls (HC) using these physiological markers.

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Area of Science:

Neuroscience and Computational Psychiatry
Biomedical Engineering and Signal Processing

Background:

Heart rate variability (HRV) reflects autonomic nervous system function and emotional regulation.
Electroencephalography (EEG) measures cortical activity linked to psychopathology.
Previous research utilized HRV and EEG, often with other wearable data, for psychiatric disorder (PT) classification, but few explored diverse diagnoses against healthy controls (HC).

Purpose of the Study:

To identify key heart rate variability (HRV) and prefrontal electroencephalography (EEG) features for accurate classification of psychiatric disorders (PT) versus healthy controls (HC) using Support Vector Machine (SVM).
To contribute to early, individual-level screening of PT and reduce the duration of untreated illness.

Main Methods:

Simultaneous 5-minute photoplethysmography (PPG) for HRV and resting-state EEG data were collected from 182 participants (87 PT, 95 HC).
Time- and frequency-domain HRV features and EEG power spectral density were analyzed.
A feature selection process using one-way ANOVA F-value identified top N (1-22) HRV/EEG features for Gaussian radial basis function kernel SVM models.
Leave-one-out cross-validation (LOOCV) assessed classification performance (balanced accuracy, AUROC) for PT vs. HC.

Main Results:

The optimal SVM model with 13 features achieved a balanced accuracy of 0.76 and an area under the receiver operating characteristic curve (AUROC) of 0.78.
Key features included HRV power spectral density (high, very low, low frequency bands, total power, SDNN) and frontal EEG alpha peak frequency and power spectral density.
These top 13 features were consistently selected in over 90% of LOOCV iterations.

Conclusions:

Combining HRV and prefrontal EEG features shows synergistic potential for machine learning-based mental health screening.
Further research is needed to predict treatment response and guide therapeutic regimens using baseline physiological markers.