HRV-Based Respiratory-Rate Estimation in Older Adults: Error Modeling and Pilot Validation for Wearable Sleep Monitoring

Vollständiger Abstract

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Respiratory frequency is a critical biomarker in sleep medicine and circadian biology. We investigated whether the high-frequency (HF) component of heart-rate variability (HRV)—which reflects respiratory sinus arrhythmia (RSA)—can serve as a non-invasive proxy for breathing rate estimation from ECG or PPG. We employed a two-stage validation design: (1) a physiologically calibrated simulation study (N = 30 per condition, five conditions, 180 s recordings) for controlled error characterization using the PhysioNet ECG-ID Database (Electrocardiogram Identification Database, DOI: 10.13026/C2XW26) as the processing pipeline reference; and (2) pilot real-data validation in N = 10 older adult participants (mean age 71.3 years) with simultaneous ECG and thermistor respiratory reference measured using the East Medic Biotope Mini (1000 Hz). Results: Under controlled resting conditions, simulation yielded MAE = 0.46 bpm (SNR = 4.8). An empirical error formula MAE = 2.017 × SNR^(−1.187) (R2 = 0.71) was derived. In the real-data validation, 4/10 participants achieved MAE ≤ 2.0 bpm; the remaining 6/10 showed errors of 6–17 bpm attributable to non-respiratory HF oscillations, harmonic confusion, and breathing rate variability. The HF-peak method is reliable when SNR is high and breathing is regular but requires additional quality criteria beyond SNR alone in older adult populations where non-respiratory HF oscillations may confound spectral peak detection.