Parchani Gaurav, Kumar Gulshan, Rao Raghavendra, Udupa Kaviraja, Saran Vibhor
Turtle Shell Technologies Pvt. Ltd, Bengaluru, Karnataka, India.
Department of Neurophysiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.
Ann Neurosci. 2022 Jan;29(1):16-20. doi: 10.1177/09727531211063426. Epub 2022 Feb 2.
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.
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).
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.
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.
The results show that HRV parameters captured using unobtrusive and non-invasive BCG sensors are comparable to HRV calculated using ECG.
自主神经系统的功能在日常生活中至关重要。心率变异性（HRV）已被证明可用于评估自主神经系统的功能。自主神经系统功能失衡与慢性疾病如慢性肾病、心血管疾病、糖尿病等相关。
通过将非接触式心冲击图（BCG）系统计算的HRV参数与使用心电图（ECG）的标准商业软件得出的参数进行比较，评估该系统的有效性。
本研究使用三通道心电图动态监测仪采集ECG信号，而BCG信号则使用由放置在研究参与者床垫下的振动声学传感器组成的BCG传感片采集。
该研究共对24名受试者进行了54次夜间记录。所提出的方法在20毫秒和30毫秒的容差范围内，分别覆盖了正常到正常间隔标准差（SDNN）的97.92%的时段和逐次差值均方根（RMSSD）的99.27%的时段，而低频（LF）与高频（HF）比值的两分钟间隔的98.84%在1的容差范围内被覆盖。还计算了肯德尔和谐系数，所有三个参数的P值均<0.001，SDNN、RMSSD和LF/HF的系数分别为0.66、0.55和0.44。
结果表明，使用不显眼且无创的BCG传感器采集的HRV参数与使用ECG计算的HRV相当。
