Why the study?
Most openly available heart rate algorithms focus on ECG data and generally do not function well on PPG data, especially noisy PPG data collected in experimental studies.
HeartPy provides a noise-resistant, open-source toolkit for analyzing PPG data, addressing the limitations of ECG-based algorithms when applied to noisy PPG signals.
HeartPy enables PPG analysis where ECG tools fail; leaves open clinical validation before routine use in monitoring or trials.
This paper describes the functioning and development of HeartPy: a heart rate analysis toolkit designed for photoplethysmogram (PPG) data. Most openly available algorithms focus on electrocardiogram (ECG) data, which has very different signal properties and morphology, creating a problem with analysis. ECG-based algorithms generally don’t function well on PPG data, especially noisy PPG data collected in experimental studies. To counter this, we developed HeartPy to be a noise-resistant algorithm that handles PPG data well. It has been implemented in Python and C. Arduino IDE sketches for popular boards (Arduino, Teensy) are available to enable data collection as well. This provides both pc-based and wearable implementations of the software, which allows rapid reuse by researchers looking for a validated heart rate analysis toolkit for use in human factors studies. Funding statement: Part of the software has been developed within the “Taking the Fast Lane” project, funded by NWO TTW¹, project number 13771.
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Gent et al. (2019) studied this question.
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