Abstract
Background: Functions of the autonomic nervous system have cardinal importance in day-to-day life. Heart rate variability (HRV) has been shown to estimate the functioning of the autonomic nervous system. Imbalance in the functioning of the autonomic nervous system is seen to be associated with chronic conditions such as chronic kidney disease, cardiovascular diseases, diabetes mellitus, and so on.
Purpose: To evaluate the efficacy of a non-contact ballistocardiography (BCG) system to calculate HRV parameters by comparing them to the parameters derived from a standard commercial software that uses an electrocardiogram (ECG).
Methods: Current study captured an ECG signal using a three-channel ECG Holter machine, whereas the BCG signal was captured using a BCG sensor sheet consisting of vibroacoustic sensors placed under the mattress of the participants of the study.
###### Results: The study was conducted on 24 subjects for a total of 54 overnight recordings. The proposed method covered 97.92% epochs of the standard deviation of NN intervals (SDNN) and 99.27% epochs of root mean square of successive differences (RMSSD) within 20 ms and 30 ms tolerance, respectively, whereas 98.84% of two-min intervals for low-frequency (LF) to high-frequency (HF) ratio was covered within a tolerance of 1. Kendall’s coefficient of concordance was also calculated, giving a P < .001 for all the three parameters and coefficients 0.66, 0.55, and 0.44 for SDNN, RMSSD, and LF/HF, respectively.
###### Conclusion: The results show that HRV parameters captured using unobtrusive and non-invasive BCG sensors are comparable to HRV calculated using ECG.
Autonomic nervous system balance, Ballistocardiography, Heart rate variability, Parasympathetic nervous system, Sympathetic nervous system
Introduction
Heart rate variability (HRV) is a non-invasive method to measure cardiac function regulated by the autonomic nervous system. It is a promising tool to assess and quantify physiological, pharmacological and pathological changes in the autonomic nervous system by detecting the changes in the time intervals between consecutive heartbeats.1 HRV analysis is traditionally done by acquiring electrocardiogram (ECG) signals and using algorithms and software that transforms these signals. ECG monitoring requires the usage of electrodes that are stuck to the subject. In clinical settings, a subject’s risk outweighs the inconvenience quite easily. However, when the subject is stable in a general ward or home setting, the inconvenience of sticking electrodes patches and the cost associated are a detriment for using ECG to measure HRV. Continuous monitoring of the HRV indices can prove to be clinically significant in cardiac-related and other lifestyle dysfunctions because it is a reliable marker to detect sympathovagal imbalance. It can certainly add value to both prognosis and management.1,2 Thus, it is very important to have techniques that measure HRV parameters and subsequently identify the changes in autonomic activity precisely for home e-health monitoring as well as clinical settings. In this context, our study used non-contact ballistocardiography (BCG). BCG is the unobtrusive and non-invasive system that evaluates cardiovascular functions and can be a possible solution as it does not require any physical monitoring by a technician, as compared with an ECG.3 BCG sensors capture the vibrations generated by contraction of the ventricles of the heart and subsequent ejection of accelerated blood into the aorta.4 BCG-based system does not require placement of any electrodes, unlike ECG, to obtain signals and can be used continuously, avoiding any uneasiness and discomfort. Although BCG systems are susceptible to noise in case of movements and posture of the subject,5 such a system is suitable for long-term continuous data acquisition and is more helpful in monitoring the autonomic nervous system. In the present study, we evaluate the efficacy of a BCG-based device placed under the mattres of the subject to determine HRV.
Earlier studies have shown that apart from signals obtained by the electrical activity of the heart, the vibrations of the heart during systole produce waveforms that correspond to R waves.6 These waveforms are also called J waves, and they tend to correlate and synchronize with QRS complexes and interbeat intervals (R-R intervals).7
In the present study, we have used a BCG-based device to extract J-J intervals to estimate the HRV indices both in time and frequency domains, further matched and correlated with the HRV indices acquired with standard ECG devices at the same time.
Read the complete document here: https://journals.sagepub.com/doi/10.1177/09727531211063426
Efficacy of Non-contact BallistocardiographySystem to Determine Heart Rate Variability - Gaurav Parchani, Gulshan Kumar, Raghavendra Rao, Kaviraja Udupa, Vibhor Saran, 2022