Abstract
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. <strong>Funding statement:</strong> Part of the software has been developed within the “Taking the Fast Lane” project, funded by NWO TTW<sup>1</sup>, project number 13771.
Highlights
In the field of transportation research one of the main goals is to get to a point where zero traffic fatalities occur [1]
Attentional failures, or driver states that are incongruent with the driving task are a major cause of traffic accidents [2]
A Python version is available for PC-based research, as well as limited implementations for several popular Arduino and ARMbased boards that assist in data collection, pre-processing and offer methods of real-time analysis
Summary
In the field of transportation research one of the main goals is to get to a point where zero traffic fatalities occur [1]. A Python version is available for PC-based research, as well as limited implementations for several popular Arduino and ARMbased boards that assist in data collection, pre-processing and offer methods of real-time analysis. PPG sensors offer a less invasive way of measuring heart rate data, which is one of their main advantages. PPG signals have the disadvantages of showing more noise, large amplitude variations, and the morphology of the peaks displays broader variation (Figure 2b, c) This complicates analysis of the signal, especially when using software designed for ECG, which the available open source tools generally are. HRV is expressed in the median absolute deviation of intervals between heart beats (MAD), the standard deviation of intervals between heart beats (SDNN), the root mean square of successive van Gent et al: Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors. Analysis Overview This section describes the architecture of the algorithm and gives an overview of how the heart rate signal is processed and analysed
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