Description of a Database Containing Wrist PPG Signals Recorded during Physical Exercise with Both Accelerometer and Gyroscope Measures of Motion

Abstract

Wearable heart rate sensors such as those found in smartwatches are commonly based upon Photoplethysmography (PPG) which shines a light into the wrist and measures the amount of light reflected back. This method works well for stationary subjects, but in exercise situations, PPG signals are heavily corrupted by motion artifacts. The presence of these artifacts necessitates the creation of signal processing algorithms for removing the motion interference and allowing the true heart related information to be extracted from the PPG trace during exercise. Here, we describe a new publicly available database of PPG signals collected during exercise for the creation and validation of signal processing algorithms extracting heart rate and heart rate variability from PPG signals. PPG signals from the wrist are recorded together with chest electrocardiography (ECG) to allow a reference/comparison heart rate to be found, and the temporal alignment between the two signal sets is estimated from the signal timestamps. The new database differs from previously available public databases because it includes wrist PPG recorded during walking, running, easy bike riding and hard bike riding. It also provides estimates of the wrist movement recorded using a 3-axis low-noise accelerometer, a 3-axis wide-range accelerometer, and a 3-axis gyroscope. The inclusion of gyroscopic information allows, for the first time, separation of acceleration due to gravity and acceleration due to true motion of the sensor. The hypothesis is that the improved motion information provided could assist in the development of algorithms with better PPG motion artifact removal performance.

GPU-Based Segmented-Beat Modulation Method for Denoising Athlete Electrocardiograms During Training

“…Measurements were taken using an Actiwave (CamNtech, Cambridge, UK) ECG recorder placed on the chest while participants were asked to perform one or more exercise tasks (walk, run, low-resistance bike and high-resistance bike) as summarized in Table 1. Data were taken from the "Wrists PPG During Exercise" database by Physionet [14,15] All TECG recordings were band-pass filtered through a bidirectional 3 rd -order Butterworth filter with cut-off frequencies of 0.5Hz and 40Hz. After that, the baseline was removed.…”

The feasibility of atrial and ventricular arrhythmias recognition using metrics of signal complexity for heartbeat intervals

“…Sometimes, the signal is so severely contaminated by the noise that only the heart rate information (e.g., from the R-R interval) can be extracted from the ECG signal. Hence, a reliable approach using the metrics derived from HRI alone to reflect the physiological/pathological status of the heart is a practical necessity (Chen et al 2011, Jarchi et al 2017.…”

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

“…One major source of HRV contamination is known to be cadence [5,3]. Recently, the study of HRV during stress test has been of interest [2,3,13,14,17,18]. In 2009, Blain et al in [5] revealed the existence of cardiolocomotion coupling (CLC) components in HRV during cycling.…”