Psychiatry research · 2026
Machine learning based identification of anxiety disorders using autonomic biomarkers: a diagnostic cross-validation study.
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Delgadillo Jaime, Ganguly Taposhri, Phillips Charlotte, Cooper Ryan, Cantwell Bod, Núñez Catalina, Levita Liat
Аннотация
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 evaluated 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.
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 evaluated 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.