QRS detection using adaptive filters: A comparative study

doi:10.1016/j.isatra.2016.09.023

QRS detection using adaptive filters: A comparative study

2017-01-01

41

PRE

AI

DOI：10.1016/j.isatra.2016.09.023

Original

Original request for help

Save

Abstract

En 中文

Electrocardiogram (ECG) is one of the most important physiological signals of human body, which contains important clinical information about the heart. Monitoring of ECG signal is done through QRS detection. In this paper, an improved QRS detection algorithm, based on adaptive filtering principle, has been designed. Enumeration of the effectiveness of various LMS variants used in adaptive filtering based QRS detection algorithm has been done through fidelity parameters like sensitivity and positive predictivity. Whole family of LMS algorithm has been implemented for comparison. Sign-sign LMS, sign error LMS, basic LMS and normalized LMS are re-implemented, while variable leaky LMS, variable step size LMS, leaky LMS, recursive least squares (RLS), and fractional LMS are novel combination presented in this paper. After analysis of the obtained results, performance of leaky-LMS algorithm is found to be the best with sensitivity, positive predictivity, and processing time of 99.68%, 99.84%, and 0.45 s respectively. Reported results are tested and evaluated over MIT/BIH arrhythmia database. Presented study also concludes that the performance of most of the variants gets affected due to low SNR but the Leaky LMS performs better even under heavy noise conditions. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Keywords:

Adaptive filtering

Adaptive thresholding

ECG

Leaky-LMS

QRS complex detection

QRS detection using adaptive filters: A comparative study

Abstract

AI Summary

Journal

Organization