Open AccessUse of zero‐frequency resonator for automatically detecting systolic peaks of photoplethysmogram signal
Author(s) -
Vadrevu Simhadri,
Manikandan M. Sabarimalai
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2018.5026
Subject(s) - photoplethysmogram , robustness (evolution) , computer science , pattern recognition (psychology) , subtraction , noise (video) , artificial intelligence , signal (programming language) , signal to noise ratio (imaging) , signal processing , mathematics , speech recognition , computer vision , telecommunications , biochemistry , chemistry , radar , arithmetic , filter (signal processing) , image (mathematics) , gene , programming language
This work investigates the application of zero‐frequency resonator (ZFR) for detecting systolic peaks of photoplethysmogram (PPG) signals. Based on the authors’ studies, they propose an automated noise‐robust method, which consists of the central difference operation, the ZFR, the mean subtraction and averaging, the peak determination, and the peak rejection/acceptance rule. The method is evaluated using different kinds of PPG signals taken from the standard MIT‐BIH polysomnographic database and Complex Systems Laboratory database and the recorded PPG signals at their Biomedical System Lab. The method achieves an average sensitivity (Se) of 99.95%, positive predictivity (Pp) of 99.89%, and overall accuracy (OA) of 99.84% on a total number of 116,673 true peaks. Evaluation results further demonstrate the robustness of the ZFR‐based method for noisy PPG signals with a signal‐to‐noise ratio (SNR) ranging from 30 to 5 dB. The method achieves an average Se = 99.76%, Pp = 99.84%, and OA = 99.60% for noisy PPG signals with a SNR of 5 dB. Various results show that the method yields better detection rates for both noise‐free and noisy PPG signals. The method is simple and reliable as compared with the complexity of signal processing techniques and detection performance of the existing detection methods.