Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network
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Date
2022
Authors
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Journal ISSN
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Publisher
UMT,Lahore
Abstract
Electrocardiogram (ECG) is the most common and low-cost diagnostic tool used in
healthcare institutes for screening heart electrical signals. The abnormal heart signals are
commonly known as arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases,
it can cause death. The arrhythmia can be of different types, and it can be detected by an
ECG test. The automated screening of arrhythmia classification using ECG beats is
developed for ages. The automated systems that can be adapted as a tool for screening
arrhythmia classification play a vital role not only for the patients but can also assist the
doctors. The deep learning-based automated arrhythmia classification techniques are
developed with high accuracy results but are still not adopted by healthcare professionals
as the generalized approach. The primary concerns that affect the success of the developed
arrhythmia detection systems are (i) manual features selection, (ii) techniques used for
features extraction, and (iii) algorithm used for classification and the most important is the
use of imbalanced data for classification