¹Seoul National University Hospital, Psychiatry, Seoul, Korea, Republic of

Background: Psychiatric care critically lacks objective biomarkers for monitoring symptom trajectories, often delaying the detection of clinical deterioration. While heart rate variability (HRV) offers promise as a continuous, non-invasive marker of autonomic function, prior prediction models have achieved only modest performance (AUC 0.55–0.65). This limitation likely stems from a reliance on static features that fail to capture the complex temporal dynamics and substantial inter-individual variability inherent in longitudinal physiological data.

Aims & Objectives: To develop and validate a comprehensive temporal feature engineering framework for HRV-based prediction of psychiatric symptom deterioration, and to identify statistically validated predictors with clinical interpretability.

Method: We analyzed longitudinal data from 193 psychiatric inpatients (633 assessments) across four symptom domains: general psychopathology (BPRS), depression (MADRS), mania (YMRS), and anxiety (HAMA). Beyond 164 standard HRV features, we engineered temporal features including delta (visit-to-visit change) and cumulative (expanding window) aggregations to capture multi-scale temporal dynamics, expanding the feature space to 600–800 features.Models were validated using TRUE nested cross-validation with rigorous four-level data leakage prevention (patient-level splitting, temporal leakage prevention, independent feature engineering, and nested feature selection). Generalized estimating equations (GEE) were employed to statistically confirm feature associations while accounting for within-patient correlation. All GEE models included adjustment for age, sex, psychiatric diagnosis, psychotropic medication class and duration, and body mass index to address potential confounding.

Results: The comprehensive temporal model achieved an average test AUC of 0.70 (95% CI: 0.654–0.746), improving substantially over a baseline model using 164 standard HRV features only (AUC 0.464; +0.236 AUC, +51%). In ablation analyses, both delta features (+0.040 AUC vs. NO Delta) and cumulative features (+0.025 AUC vs. Markov) contributed independently to predictive accuracy. GEE analysis with Bonferroni correction (adjusted α = 0.0125 for 4 symptom scales) identified cumulative HRV instability—particularly Mean RR cumulative standard deviation—as the strongest predictor of deterioration (OR = 1.98 for depression, OR = 1.61 for mania; both p < 0.01). Higher parasympathetic activity (HF power) showed a protective effect against manic deterioration (OR = 0.67, p < 0.001). The number needed to harm (NNH) for 1-SD increase in Mean RR cumulative standard deviation for depressive deterioration was 3.1 (95% CI: 2.3–4.8), indicating clinically meaningful magnitude. Cross-validation estimates closely matched test performance (gap = +0.034), supporting robust generalization without major data leakage.

Discussion & Conclusions: Temporal feature engineering substantially improves HRV-based prediction of psychiatric symptom deterioration by effectively capturing within-subject dynamics. Cumulative HRV instability emerges as a novel transdiagnostic biomarker, offering a pathway for integrating objective, wearable-based monitoring into proactive clinical decision support systems.