HRV features from wearable ECG distinguished patients with psychiatric disorders from healthy controls with 80.0% accuracy, and lower HRV triangular index correlated with symptom severity (p=0.045).
Case-Control (n=60)
Can heart rate variability (HRV) features derived from wearable ECG recordings distinguish patients with schizophrenia or bipolar disorder from healthy controls?
Wearable ECG-derived HRV features, particularly the HRV triangular index, can classify psychiatric disorders with 80% accuracy and correlate with symptom severity.
Objective assessment of psychiatric conditions using wearable sensing is becoming increasingly important for intelligent healthcare systems. This study examined whether heart rate variability (HRV) features derived from electrocardiogram (ECG) recordings measured during daily activities can distinguish individuals with psychiatric disorders from healthy controls and whether the identified key HRV feature is associated with symptom severity. Thirty patients with schizophrenia or bipolar disorder and 30 age-and sex-matched healthy controls wore a wearable ECG sensor for approximately 1.5–2 hours during habitual daily activities. Twenty-one HRV features including time-, frequency- and nonlinear-domain metrics were extracted from the ECG recordings. A random forest classifier was trained and evaluated using stratified five-fold cross-validation, achieving an overall accuracy of 80.0%. Permutation-based feature importance highlighted the HRV triangular index as a major contributor to classification performance, and a lower triangular index was significantly associated with higher PANSS general psychopathology scores (r = −0.44, Bonferroni-corrected p = 0.045). These findings demonstrate the feasibility of interpretable HRV-based classification in daily-life settings and suggest that reduced HRV may reflect clinically meaningful symptom burden in psychiatric disorders.
Bhuiyan et al. (Mon,) conducted a case-control in Schizophrenia or bipolar disorder (n=60). Wearable ECG sensor for HRV feature extraction vs. Healthy controls was evaluated on Overall classification accuracy distinguishing psychiatric patients from healthy controls. HRV features from wearable ECG distinguished patients with psychiatric disorders from healthy controls with 80.0% accuracy, and lower HRV triangular index correlated with symptom severity (p=0.045).