Wearable ECG-Derived Respiration Performance for Respiratory Monitoring with a Non-Standard ECG Lead

Continuous cardiorespiratory monitoring is crucial for understanding physiological conditions, particularly respiratory and cardiac diseases. Wearable devices offer an attractive approach for this goal, allowing unobtrusive data collection. This study evaluates two ECG-derived respiration (EDR) algorithms using non-standard electrocardiogram (ECG) leads from a wearable device, and as well as bioimpedance signal for extracting breathing information. The performance is compared against respiratory airflow. 12 healthy volunteers followed a respiratory protocol involving free and paced breathing while ECG, bioimpedance and respiratory airflow were acquired. ECG and bioimpedance were measured using a wearable device, whereas, airflow was recorded using a standard system. Strong linear relationships (Pearson coefficients > 0.90) were observed between EDR signals and respiratory volume, outperforming bioimpedance. The R-wave amplitude algorithm exhibited superior accuracy and lower errors (< 5 %) in respiratory cycle detection. Continuous monitoring remained unaffected over two days. The findings contribute to advancing wearable-based respiratory monitoring techniques for clinical and research applications.