Deep Learning-Based Arrhythmia Detection in Electrocardiograph

Yang Meng,Mei Yue,Guoxin Liang,Gustavo Ramirez

doi:10.1155/2021/9926769

Yang Meng, Mei Yue + Show 2 more

Open Access

https://doi.org/10.1155/2021/9926769

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Abstract

This study aimed to explore the application of electrocardiograph (ECG) in the diagnosis of arrhythmia based on the deep convolutional neural network (DCNN). ECG was classified and recognized with the DCNN. The specificity (Spe), sensitivity (Sen), accuracy (Acc), and area under curve (AUC) of the DCNN were evaluated in the Chinese Cardiovascular Disease Database (CCDD) and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, respectively. The results showed that in the CCDD, the original model tested by the small sample set had an accuracy (Acc) of 82.78% and AUC of 0.882, while the Acc and AUC of the translated model were 85.69% and 0.893, respectively, so the difference was notable ( P < 0.05); the Acc of the original model and the translated model was 80.12% and 82.63%, respectively, in the large sample set, so the difference was obvious ( P < 0.05). In the MIT-BIH database, the Acc of normal (N) heart beat (HB) (99.38%) was higher than that of the atrial premature beat (APB) (87.45%) ( P < 0.05). In a word, applying the DCNN could improve the Acc of ECG for classification and recognition, so it could be well applied to ECG signal classification.

Highlights

With the rapid development of society and economy in recent years, people’s life rhythm has accelerated, and the increasing pressure of survival has increased the prevalence of cardiovascular disease (CVD) year by year, threatening the human life and health seriously [1]
Most CVD patients are accompanied by arrhythmia, and most of which suffered from some chronic diseases. erefore, effective detection and diagnosis of arrhythmia and early prevention of CVD are of great clinical significance
The ECG is still the final important means to diagnose arrhythmia. e traditional ECG analysis is mainly based on the naked eye observation of clinicians, and the analysis is based on personal experience and existing theoretical knowledge, which may lead to misjudgment of the results and cause serious impact on patients and hospitals [6, 7]. erefore, the automatic analysis and diagnosis technology of ECG comes into being

Summary

Introduction

With the rapid development of society and economy in recent years, people’s life rhythm has accelerated, and the increasing pressure of survival has increased the prevalence of cardiovascular disease (CVD) year by year, threatening the human life and health seriously [1]. Erefore, prevention and treatment of CVD are very important. Most CVD patients are accompanied by arrhythmia, and most of which suffered from some chronic diseases. Erefore, effective detection and diagnosis of arrhythmia and early prevention of CVD are of great clinical significance. Erefore, the automatic analysis and diagnosis technology of ECG comes into being. Many scholars have proposed a variety of automatic ECG classification algorithms. Xu et al (2018) [8] proposed a framework combining the improved frequency slice wavelet transform (FSWT) and CNN, which could identify the atrial fibrillation or nonatrial fibrillation on the ECG automatically and accurately; the research process could be divided into four parts: ECG signal acquisition, signal denoising, signal feature extraction, and ECG signal classification and recognition [9]. CNN is a discriminative deep learning model used for image signal

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