Research Article
Approximating Heart Rate Variability
@INPROCEEDINGS{10.4108/ICST.PERVASIVEHEALTH2008.2552, author={Jen-Liang Cheng and Jin-Ren Jeng and Zhu-Xuan Lin and Jiunn-Horng Lee}, title={Approximating Heart Rate Variability}, proceedings={2nd International ICST Conference on Pervasive Computing Technologies for Healthcare}, publisher={IEEE}, proceedings_a={PERVASIVEHEALTH}, year={2008}, month={7}, keywords={Heart Rate Variability; Robustness; ECG; Approximation}, doi={10.4108/ICST.PERVASIVEHEALTH2008.2552} }- Jen-Liang Cheng
Jin-Ren Jeng
Zhu-Xuan Lin
Jiunn-Horng Lee
Year: 2008
Approximating Heart Rate Variability
PERVASIVEHEALTH
ICST
DOI: 10.4108/ICST.PERVASIVEHEALTH2008.2552
Abstract
Heart rate variability (HRV) measurement in the field has not been widely studied due to the presence of substantial noises in certain circumstances even after signal processing. To overcome such a difficulty, a method, called VACA (Vote-And- Chain Algorithm) is proposed to obtain an approximate HRV measurement. With VACA, the contaminated ECGs can be patched to obtain HRV metric, such as SDNN, even when the arrival rate of noises has reached the same level of heart rate. The performance of this algorithm is evaluated with 27,000 contaminated ECGs which are synthesized by real ECGs in the Physio-Net and noises of Poisson process. The best parameters for VACA are explored so that it can reach an accuracy of (100±20)% for 97% of the 27000 contaminated ECG data. The experiment results show that VACA is an robust method for HRV measurement in applications that long-term multi-lead ECG is not feasible.