Adaptive Wavelet Based Identification and Extraction of PQRST Combination in Randomly Stretching ECG Sequence

TL;DR

This paper introduces an adaptive wavelet-based method for highly accurate detection and classification of PQRST ECG waveforms, even in sequences with random stretching, enhancing automated cardiovascular diagnostics.

Contribution

The paper presents a novel adaptive wavelet approach that accurately detects and classifies PQRST features in ECG signals with random variations, improving over existing methods.

Findings

R-peak detection accuracy of 99.99%

Effective tagging of P, Q, S, T waves

Robust performance in randomly stretched ECG sequences

Abstract

Cardiovascular system study using ECG signals have evolved tremendously in the domain of electronics and signal processing. However, there are certain floating challenges unresolved in the analysis and detection of abnormal performances of cardiovascular system. As the medical field is moving towards more automated and intelligent systems, wrong detection or wrong interpretations of ECG waveform of abnormal conditions can be quite fatal. Since the PQRST signals vary their positions randomly, the process of locating, identifying and classifying each feature can be cumbersome and it is prone to errors. Here we present an automated scheme using adaptive wavelet to detect prominent R-peak with extreme accuracy and algorithmically tag and mark the coexisting peaks P, Q, S, and T with almost same accuracy. The adaptive wavelet approach used in this scheme is capable of detecting R-peak in ECG with 99.99% accuracy along with the rest of the waveforms.