Designing an Optimum and Reduced Order Filter for Efficient ECG QRS Peak Detection and Classification of Arrhythmia Data

Hemant Amhia,A K Wadhwani,Kalidoss Rajakani

doi:10.1155/2021/6542290

Abstract

Electrocardiogram (ECG) is commonly used biological signals that show an important role in cardiac analysis. The interpretation and acquisition of QRS complex are significant measures of ECG data dispensation. The R wave has a vital character in the analysis of cardiac rhythm irregularities as well as in the determination of heart rate variability (HRV). This manuscript is proposed to design a new artificial-intelligence-based approach of QRS peak detection and classification of the ECG data. The design of reduced order IIR filter is proposed for the low pass smoothening of the ECG signal data. The min-max optimization is used for optimizing the filter coefficient to design the reduced order filter. In this research paper, elimination of baseline wondering and the power line interferences from the ECG signal is of main attention. The result presented that the accuracy is increased by around 13% over the basic Pan–Tompkins method and around 8% over the existing FIR-filter-based classification rules.

Highlights

It has been found by the World Health Organization that heart arrest is the world’s most common cause for death [1].erefore, a strong focus has been put on cardiac health research with a focus on medicine, prevention, and technology which sequentially led investigators to work on educating cardiovascular skills that are usually applied in clinics and hospitals to make predictable diagnoses.erefore, ECG signal analysis in the clinical heart test used to screen various heart defects is of prime importance
In this paper the preprocessing stage is followed through the Hilbert transform stage. e filtered ECG signal is passed to the Hilbert transform block in order to improve the efficiency of the peak detection method. e basic uses of the Hilbert transform (HT) is to improve the efficacy of the R peak recognition for envelop detection. e use of Hilbert Transform (HT) [18] usually shifts positive and negative frequency components by −90° and +90°, respectively; on the other hand, all the amplitudes of transform domain function F[x(t)] remains constant as the R peaks of the ECG
E time history analysis plays impactful role for heart rate variability (HRV) detections. erefore, this paper proposed to design a new approach of QRS peak detection and classification of the ECG data. e design of reduced order IIR filter is proposed for the low pass smoothening of the ECG signal data. e min-max optimization is used for optimizing the filter coefficient for designing the reduced order filter design

Summary

Introduction

Erefore, a strong focus has been put on cardiac health research with a focus on medicine, prevention, and technology which sequentially led investigators to work on educating cardiovascular skills that are usually applied in clinics and hospitals to make predictable diagnoses. Erefore, ECG signal analysis in the clinical heart test used to screen various heart defects is of prime importance. Electrocardiogram (ECG) is an irregular signal that replicates cardiac activities. Most understanding of heart pathology is possible by studying ECG signal [1]. E evaluation matrix for healthy heart is heart rate and ECG signals. If we capture ECG signal from a patient and if there is any nonlinearity, this is termed as cardiac arrhythmia [2].

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Results

Conclusion