Automatic localisation of ECG signal features for detection of abnormalities is recently flashed a remarkable research work. However with the promising result of the analysis there is much space of improvements in terms of accuracy. In this work a part of ventricular activity in cardiac cycle is addressed to establish the morphological characteristics of the segmental ECG signal. Here ventricular QRS complex of the ECG signal is analysed in frequency domain by Variational Mode Decomposition (VMD) technique. The purpose of this work is to extract the QRS complex from raw ECG data without pre-processing. This method is tested on MIT-BIH arrhythmia data base and a comprehensive result is established with an accuracy of more than 99%.

SAURABH PAL

Applied Physics

MADHUCHHANDA MITRA

U MAJI

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

IET Conference Publications

Most of the non-stationary signals need adaptive processing technique for denoising, signal processing for feature extraction and analysis. In this regard, signal decomposition methods plays a vital role as selective reconstruction extracts the enhanced version of the signal buried in the noise. Decomposition mode based analysis also becomes popular especially in case of biosignals due to their highly non-stationary nature. Biosignals are better decomposed by a technique where basis function is derived from the signal itself. This data adaptive decomposition of biosignals into different frequency modes is very effective irrespective of multiple periodicities present in the signal or unknown sampling rate. This paper aims to study the performance of Empirical Mode Decomposition (EMD) and the Variational Mode Decomposition (VMD) technique over the popular ECG signal in terms of different periodicities during various cardiac abnormalities. The results highlight the main differences between the methods in range of signal decomposition levels as well as ability of extracting both low and high frequency from the signal. © 2016 IEEE.

2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016

Empirical mode decomposition vs. variational mode decomposition on ECG signal processing: A comparative study

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively. © 2013 IEEE.

IEEE Transactions on Instrumentation and Measurement

Application of cross wavelet transform for ECG pattern analysis and classification

In this paper an Empirical Mode Decomposition (EMD) based ECG signal enhancement and QRS detection algorithm is proposed. Being a non-invasive measurement, ECG is prone to various high and low frequency noises causing baseline wander and power line interference, which act as a source of error in QRS and other feature extraction. EMD is a fully adaptive signal decomposition technique that generates Intrinsic Mode Functions (IMF) as decomposition output. Here, first baseline wander is corrected by selective reconstruction based slope minimization technique from IMFs and then high frequency noise is removed by eliminating a noisy set of lower order IMFs with a statistical peak correction as high frequency noise elimination is accompanied by peak deformation of sharp characteristic waves. Then a set of IMFs are selected that represents QRS region and a nonlinear transformation is done for QRS enhancement. This improves detection accuracy, which is represented in the result section. Thus in this method a single fold processing of each signal is required unlike other conventional techniques. © 2011 Elsevier Ltd.

Journal	IET Conference Publications
Publisher	Institution of Engineering and Technology
Open Access	No