Key result
Adding insomnia scores to ECG heart rate variability improves severe depression detection to ~0.84 AUROC.
Why the study?
Short-term ECG-derived heart rate variability provides objective autonomic markers in depression, but it may not fully capture the clinical burden of depressive symptoms. The incremental value of adding insomnia symptoms to an interpretable machine learning framework for classifying severe depressive symptom burden needed evaluation.
Does integrating HAMD-derived insomnia scores with short-term ECG-derived heart rate variability in a machine learning framework improve the classification of severe depressive symptom burden in psychiatric outpatients?
Observational (n=2,663)
Does integrating HAMD-derived insomnia scores with short-term ECG-derived heart rate variability in a machine learning framework improve the classification of severe depressive symptom burden in psychiatric outpatients?
Answers are grounded in this paper and cite their sources.
Integrating insomnia symptom scores with short-term ECG-derived heart rate variability in an explainable machine learning model significantly improves the classification of severe depressive symptom burden in psychiatric outpatients.
BACKGROUND Depression is a common mental health condition requiring repeated symptom severity assessment. While short-term electrocardiography (ECG)-derived heart rate variability (HRV) provides objective autonomic markers, it may not fully capture the clinical burden of depressive symptoms. We developed an interpretable machine learning framework combining short-term ECG-derived HRV and evaluated the incremental value of insomnia symptoms for classifying severe depressive symptom burden (Beck Depression Inventory-II ≥ 29). METHODS We analyzed 2663 psychiatric outpatients. Seven classifiers, a penalized logistic regression baseline, and three voting ensembles were evaluated using stratified 5-fold cross-validation with validation-selected decision thresholds. Feature ablation, decision curve analysis, and explainable artificial intelligence (SHAP and LIME) examined the contributions of HRV and Hamilton Depression Rating Scale (HAMD)-derived insomnia score. RESULTS The weighted soft-voting ensemble achieved the highest performance, with an area under the receiver operating characteristic curve (AUROC) of 0.837 (95%CI, 0.821-0.852), accuracy 0.756, sensitivity 0.721, specificity 0.776, and F1 score 0.686. Adding the insomnia score improved ensemble AUROC from 0.701 to 0.837 (Δ = 0.136, P < 0.001), while adding HRV features to age, gender, and insomnia score increased AUROC from 0.737 to 0.837 (Δ = 0.100, P < 0.001). Excluding nine duration-sensitive HRV indices yielded an AUROC of 0.787. SHAP and LIME identified age, insomnia score, and mean heart rate as primary contributors, confirming patient-level interpretability. CONCLUSIONS Adding insomnia score to ECG-derived HRV appears to improve the classification of severe depressive symptom burden over HRV alone. This interpretable, low-burden framework shows potential clinical utility, suggesting that it may serve as an adjunctive screening tool for depression care.
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Choi et al. (2026) conducted an observational in Severe depressive symptom burden (Beck Depression Inventory-II ≥ 29) (n=2,663). Explainable machine learning framework integrating ECG-derived heart rate variability and HAMD-derived insomnia score vs. Machine learning models using ECG-derived HRV alone or age, gender, and insomnia score without HRV was evaluated on Classification of severe depressive symptom burden (Beck Depression Inventory-II ≥ 29) measured by AUROC (AUROC 0.837, 95% CI 0.821-0.852, p=< 0.001). Adding HAMD-derived insomnia score to ECG-derived HRV in a soft-voting ensemble improved classification of severe depression (AUROC 0.837 vs 0.701; P<0.001).
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