Approximating Heart Rate Variability

Author

Cheng, Jen-Liang ; Jeng, Jin-Ren ; Lin, Zhu-Xuan ; Lee, Jiunn-Horng

Author_Institution

Dept. of Med. Inf., Tzu-Chi Univ., Hualien

fYear

2008

fDate

Jan. 30 2008-Feb. 1 2008

Firstpage

323

Lastpage

326

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 (100plusmn20)% 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.

Keywords

electrocardiography; medical signal processing; stochastic processes; ECG; Physio-Net; Poisson process; electrocardiogram; heart rate variability; vote-and-chain algorithm; Biomedical informatics; Cardiology; Circuit noise; Cities and towns; Electrocardiography; Heart rate variability; Noise measurement; Noise robustness; Pollution measurement; Signal processing algorithms; Approximation; ECG; Heart Rate Variability; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing Technologies for Healthcare, 2008. PervasiveHealth 2008. Second International Conference on

Conference_Location

Tampere

Print_ISBN

978-963-9799-15-8

Type

conf

DOI

10.1109/PCTHEALTH.2008.4571103

Filename

4571103