ECG Database for Evaluating the Efficiency of Recognizing Dangerous Arrhythmias

Манило, Л. А.; Nemirko, A. P.; Evdakova, Ekaterina G.; Татаринова, А. А.

doi:10.1109/csgb53040.2021.9496029

Cited by 6 publications

(1 citation statement)

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“…The structure and functioning of heart can be studied with the help of Electrocardiography (ECG) which is an efficient technique used due to ease of access, non-invasiveness and less expensive [4]. During each heart beat it presents the depolarization and re polarization of muscles of heart is shown in the form of electrophysiological pattern.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cardiac Arrhythmia Using Multi-Perspective Convolutional Neutral Network for ECG Heartbeat Classification

Indira¹,

Lakshmi²,

Markapudi³

et al. 2022

RIA

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The leading death cause all over the world is heart disease. The presence of arrhythmias has to be examined to detect heart disease in early stage. The abnormality in heart beat rhythm is known as Arrhythmia. The speed of heart beat can be detected by Arrhythmia, it may be too slow, too fast or irregular patterns of heart beat are considered as Arrhythmia and there are various types of Arrhythmia. The electrocardiogram (ECG) produces signals, classification of such signals is very crucial for knowing the irregularity in the patterns of heart beat. As detection of arrhythmia is a challenging task, there is a great demand for an automatic detection technique to identify abnormal signals produced by heart which cannot be done manually. Therefore this paper provides a method for detection of Cardiac Arrhythmia using Multi-Perspective Convolutional Neutral Network (MPCNN) for ECG Heartbeat Classification. Basing on Physionet's MIT-BIH Arrhythmia Dataset the signals of ECG arrhythmia can be categorized into five classes. Number of layers, filters and size of the filter are appropriate parameters which are heuristically optimized for operating swiftly for operation of MPCNN effectively. Compared with the ultra-modern methods as Quantum Neural Networks and Deep Convolutional Neural Networks, the proposed method results in efficient performance having high level accuracy of 96.46%, 98.1% and 96.2% are the F1 scores for SVP and PVC respectively. The effective detection of heartbeat rhythm and irregularities can be identified using this model.

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Section: Introductionmentioning

confidence: 99%