An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network.

Yunyi Chen,Dengqing Zhang,Yuxuan Chen,Ming Chen,Gu Xiaoqing,Shengyi Ye,Wenyu Cai

doi:10.1155/2021/7167891

Abstract

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors' consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

Highlights

According to the latest World Health Statistics 2019 [1] report, heart disease, the top killer of humanity, was the primary cause of death worldwide in the past two decades, accounting for 16% of all causes of death
The ECG dataset is divided into five categories to realize a rough assessment of the heart state, providing an essential and reliable reference for the doctor’s further diagnosis. e proposed method is used to classify based on all datasets. e results show that our classifier achieved an average accuracy of 99.76%, an average sensitivity of 94.45%, an average specificity of 99.54%, and an average positive prediction rate of 97.40%
Our proposed method achieved 99.76% average accuracy, 94.45% average sensitivity, 99.54% average specificity, and 97.40% average positive prediction rate based on all samples

Summary

Introduction

According to the latest World Health Statistics 2019 [1] report, heart disease, the top killer of humanity, was the primary cause of death worldwide in the past two decades, accounting for 16% of all causes of death Since this kind of disease severely contributes to a lower life expectancy, the detection and diagnosis of cardiovascular diseases perform an inestimable value for all human beings. Many scholars have applied various algorithms to help detect these diseases, improving the classification model in accuracy, speed, and robustness. Many popular methods, such as decision trees, random forest, and SVM, are proposed in ECG data classification. To evaluate the proposed model, we compared the results of other deep learning algorithms to detect VEB and SVEB, and the proposed method obtained better results

Literature Review

Materials

Methodology

Results and Discussion

Methods

Conclusion