Abstract

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat.

Highlights

  • Cardiovascular disease (CVD) has become one of the main causes of death worldwide in the past decade [1]

  • EXPERIMENTAL RESULTS This study utilized the OpenCV software to perform a series of image processing techniques on the ECG for the extraction of the ECG curve to use it as the input to the subsequent classification model

  • This section will show the experimental results of the ECG curve extraction and the heartbeat classification experiments

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Summary

Introduction

Cardiovascular disease (CVD) has become one of the main causes of death worldwide in the past decade [1]. The vast majority of heart diseases include chronic diseases and often have symptoms of arrhythmia [2]. Real-time and accurate arrhythmia recognition can provide doctors with accurate information which can effectively prevent the occurrence of heart disease [3] and provide a targeted treatment program for patients with heart disease. Electrocardiogram (ECG) is the most commonly used clinical method for diagnosing heart disease [4]-[5]. Cardiologists have limited time to spend in analyzing millions of heartbeats of patients [6]-[7].

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