Movement filtered heart rate variability (HRV) data from a chest-worn sensor

Abstract

Recording of heart rate variability (HRV) is a noninvasive and continuous measurement method that allows investigating the autonomic nervous system (ANS) and its reaction to environmental influences. For a precise measurement of HRV data, a carefully chosen study design and environment is required to minimize secondary influences. One major influence to be avoided is movement. However, in the daily routine and for some scientific questions, movement can often not be avoided. If so, a manual or automated method to differentiate between artifacts caused by body movement and the actual psychophysiological effect is needed to ensure the data quality. In this approach, a chest-worn sensor was developed, that measures the heart rate using a single lead ECG and filters the measured change of the HRV caused by movement. Data from an integrated accelerometer is used to detect upper body movements that affect the resting heart rate. The movementcorresponding time stamps are then used to filter the Interbeat Intervals (IBI) accordingly. Functionality and effectiveness of the sensor system have been tested against state-of-the art sports- or clinical devices in varying scenarios. As our test series showed, motion filtering has a decisive effect when motion occurs, two-thirds of all cases showed a significant effect of motion filtering, with small to medium effect sizes for the parameters SD2, SD2/SD1, and SDNN. Thereby, automatic filtering of motion artifacts can help to significantly reduce the need for costly post-processing of distorted data sets. The results show a better data quality of HRV measurement, a method that is commonly used for the investigation of physiological processes in the field of chronic pain, psychology, psychiatry, or sports medicine.

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“…As a side effect, one might have special requirements regarding data privacy and protection. As a scientific institution in the biomedical field that has to deal with specific needs and research questions, financial restrictions, and sensitive data retrieved from the sensors, the above-mentioned aspects lead to many software and IoT-related hardware projects [1][2][3][4]. One of our medical projects intends to measure heart rate variability.…”

“…One of our medical projects intends to measure heart rate variability. The sensor has to exceed the precision of a clinical-grade ECG device, but at the same time has to be wireless, to be worn on the body, waterproof, heat resistant, and able to resist chemical disinfection [2].…”