Key result
The fusion of proposed subband complexity and spectral descriptor features with benchmark HRV metrics significantly improved stress and anxiety classification accuracy compared to benchmark features alone.
Why the study?
Wearable-based HRV tracking of stress and anxiety faces noise, artifacts, and confounding from mental fatigue and physical activity in uncontrolled, ecological settings.
Do novel HRV measures based on subband tachogram complexity and spectral characteristics improve the accuracy of stress and anxiety prediction compared to benchmark HRV metrics in uncontrolled settings?
Population
Subjects from two separate in-the-wild datasets
Comparison
Proposed subband complexity and spectral HRV features vs benchmark HRV metrics
Authors
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May support stress monitoring in first responders; leaves open robust validation amid real-world artifacts.
Observational (n=260)
Do novel HRV measures based on subband tachogram complexity and spectral characteristics improve the accuracy of stress and anxiety prediction compared to benchmark HRV metrics in uncontrolled settings?
p-value: p=<0.05
Novel HRV features based on subband tachogram complexity and spectral characteristics improve the accuracy of stress and anxiety monitoring in uncontrolled, real-world settings.
Tiwari et al. (2021) conducted an observational in Stress and Anxiety (n=260). Subband complexity and spectral descriptor HRV features vs. Benchmark HRV features was evaluated on Classification performance (Balanced Accuracy, F1-score, Matthews correlation coefficient) for stress and anxiety (p=<0.05). The fusion of proposed subband complexity and spectral descriptor features with benchmark HRV metrics significantly improved stress and anxiety classification accuracy compared to benchmark features alone.
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