Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.

Yinsheng Ji,Wendong Xiao,Sen Zhang

doi:10.3390/s19112558

Yinsheng Ji, Wendong Xiao + Show 1 more

Open Access

https://doi.org/10.3390/s19112558

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Abstract

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher.

Highlights

An electrocardiogram (ECG) as a cardiac activity record provides important information about the state of the heart [1]
We propose an ECG classification method using faster regions with a convolutional neural network (Faster R-CNN) with ECG images
We proposed an effective ECG classification method using Faster R-CNN based on a ZF net with ECG images as input

Summary

Introduction

An electrocardiogram (ECG) as a cardiac activity record provides important information about the state of the heart [1]. ECG arrhythmia detection is necessary for early diagnosis of heart disease patients. It is very difficult for a doctor to analyze an electrocardiogram with a long recording time for a limited time. People are almost unable to recognize the morphological changes of ECG signals without tool support. An effective computer-aided diagnosis system is needed to solve this problem. Most ECG classification methods are mainly based on one-dimensional ECG data. These methods usually need to extract the waveform’s characteristics, the interval of adjacent wave, and the amplitude and period of each wave as input. The main difference between them is the selection of the classifier

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