Key result
ECG machine learning classifier identifies anxiety disorders with ~0.71 AUC, outperforming PPG-based detection.
Why the study?
The validity and reliability of autonomic biomarkers derived from heart rate variability to classify anxiety disorder symptoms were not established.
Can a machine learning classifier using autonomic HRV biomarkers accurately identify anxiety disorders?
Cross-Sectional (n=124)
Yes
Can a machine learning classifier using autonomic HRV biomarkers accurately identify anxiety disorders?
Effect estimate: AUC 0.71
Machine learning applied to ECG-derived heart rate variability offers a moderately accurate method for objective, real-time identification of anxiety disorders.
May support ECG-ML screening for anxiety; hypothesis-generating and requires larger prospective validation before clinical use.
Background This study aimed to evaluate the validity and reliability of autonomic biomarkers derived from heart rate variability (HRV) to correctly classify cases with and without anxiety disorder symptoms. Methods This multi-center diagnostic cross-validation study involved 124 participants at two research labs, Sussex and Sheffield. Participants were exposed to a three-phase anxiety induction paradigm lasting 9 minutes (3 min. rest, 3 min. induction, 3 min. recovery). Participants completed measures of anxiety (GAD-7) and depression (PHQ-9) prior to the lab task. Their HRV was measured continuously using: [1] an industry-standard electrocardiogram (ECG) and [2] a miniaturized photoplethysmography (PPG) biosensor. Correlations between [1] and [2] were examined. A machine learning classifier was developed using ECG and PPG-derived features in a training sample from Sussex (n=88) to identify cases with anxiety disorders (GAD-7≥8). Classification accuracy was examined using the area-under-the-curve (AUC) in the validation sample from Sheffield (n=36). Correlations using dimensional scores were used to evaluate the machine learning model’s convergent (GAD-7) and discriminant validity (PHQ-9). Results HRV features from PPG and ECG were strongly correlated across all three phases (r ≥ .88, p < .001). An ECG-based machine learning classifier generalized to an external validation sample (AUC= .71), correctly classifying anxiety disorder cases with adequate sensitivity (88%) and specificity (61%) and outperforming a PPG-based classifier. As expected, the classifier had marginally higher correlations with GAD-7 ( r = .51) than PHQ-9 ( r = .47). Conclusion Biomarker data processed using a machine learning algorithm converges with an established measure of anxiety. This supports the potential use of HRV-derived biomarkers to monitor changes in real-time, which could be highly informative for exposure-based therapies.
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Delgadillo et al. (2026) conducted a cross-sectional in Anxiety disorders (n=124). ECG-based machine learning classifier vs. PPG-based classifier was evaluated on Classification accuracy (AUC) to identify cases with anxiety disorders (GAD-7≥8) in a validation sample (AUC 0.71). An ECG-based machine learning classifier correctly identified anxiety disorder cases with an AUC of 0.71, 88% sensitivity, and 61% specificity, outperforming a PPG-based classifier.
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