Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network

Ali Haider Khan,Muzammil Hussain,Muhammad Kamran Malik,Atif Khan

doi:10.1155/2021/5512243

Ali Haider Khan, Muzammil Hussain + Show 2 more

Open Access

https://doi.org/10.1155/2021/5512243

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Abstract

Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of ECG. Single Shoot Detection (SSD) MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities (i.e., myocardial infarction, abnormal heartbeat, previous history of MI, and normal class) with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder.

Highlights

According to the Centers for Disease Control and Prevention (CDC) and the American Health Monitoring Organization, the leading cause of death is cardiovascular disease [1]
DNN is best suited for medical images problem; that is the main reasons authors used Single Shoot Detection (SSD) MobileNet V2, a deep neural network-based architecture to detect the cardiac disorder on 12-lead-based ECG images
E deep neural network has proven its capabilities in various applications of image processing and computer vision. is paper critically discusses the related work and analysis, on cardiac disease detection. e study proposes a generalized methodology to process all formats of ECG

Summary

Introduction

According to the Centers for Disease Control and Prevention (CDC) and the American Health Monitoring Organization, the leading cause of death is cardiovascular disease [1]. E most common heart disease detection technique is based on electrocardiogram (ECG), angiography screening, and blood test. Researchers [4,5,6] investigated many techniques for automatically detecting cardiovascular disease using machine and deep learning techniques, typically by using ECG in one or twodimensional voltage amplitude data represented as time-series signals. Sharma et al [8] used time frequency-based ECG signals for feature extraction of the eigenvalue decomposition of Hankel matrix and Hilbert transform and used the random forest to detect the cardiovascular disorder. The main focus is to provide a novel automatic detection tool relatively similar and adaptable for the cardiac hospitals to process the 12-lead-based ECG images. Automated cardiac disorder detection via a deep neural network using 12-lead-based ECG image processing is critical.

Related Work

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