RapidHRV: an open-source toolbox for extracting heart rate and heart rate variability

Area of Science:

• Psychophysiology
• Biomedical Signal Processing
• Computational Biology

Background:

• Heart rate (HR) and heart rate variability (HRV) are crucial metrics for understanding psychophysiological states.
• Research increasingly utilizes HR/HRV from laboratory (ECG, pulse oximetry) and real-world (wearable photoplethysmography) sources.
• Traditional artifact removal in HR/HRV signals relies on manual visual inspection, which is time-consuming and subjective.

Purpose of the Study:

• To introduce RapidHRV, an open-source Python package for streamlined HR and HRV analysis.
• To provide automated preprocessing, analysis, and visualization tools for HR/HRV data.
• To evaluate the performance of RapidHRV in handling noisy signals and various data sources.

Main Methods:

• Development of an open-source Python package, RapidHRV, with automated cleaning modules.
• Testing RapidHRV on simulated datasets with controlled noise levels and sampling rates.
• Validation of RapidHRV using real-world electrocardiography (ECG), finger photoplethysmography (fPPG), and wrist photoplethysmography (wPPG) recordings.

Main Results:

• RapidHRV demonstrated excellent HR recovery across various noise levels (>=10 dB) in simulated data.
• Moderate-to-excellent HRV recovery was observed even with low signal-to-noise ratios (>=20 dB) and sampling rates (>=20 Hz).
• Validation showed good-to-excellent HR/HRV recovery for ECG and fPPG; wPPG results were sensitive to motion artifacts.

Conclusions:

• RapidHRV offers an efficient and automated solution for HR and HRV analysis.
• The package effectively processes noisy physiological signals, improving data quality.
• While robust for ECG and fPPG, RapidHRV's performance in wPPG is affected by motion, highlighting the need for motion artifact mitigation strategies.