ECG arrhythmia classification by using a recurrence plot and convolutional neural network

Mathunjwa, Bhekumuzi; Lin, Yin-Tsong; Lin, Chien-Hung; Abbod, Maysam F.; Shieh, Jiann-Shing

doi:10.1016/j.bspc.2020.102262

Cited by 137 publications

(51 citation statements)

References 56 publications

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“…Houssein et al [ 3 ] presented a new morphological features descriptor and proposed a method based on a metaheuristic algorithm termed Manta ray foraging optimization (MRFO) and SVM, obtaining 98.26% accuracy and 97.43% sensitivity. Mathunjwa et al [ 4 ] converted 1D ECG signals into 2D segments, combined recurrence plot (RP) and CNN to make arrhythmia classification, and achieved the accuracy of 95.3% on ventricular fibrillation (VF) categories and 98.41% on the atrial fibrillation (AF), normal, premature AF, and premature VF categories. Pirova et al [ 5 ] compared random forest, decision tree, and convolutional neural network algorithms, showing that the neural network is superior to other algorithms in ECG data classification, with an accuracy rate of 93.47%.…”

Section: Literature Reviewmentioning

confidence: 99%

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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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.

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Section: Literature Reviewmentioning

confidence: 99%

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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“…In [43], ECG signals were segmented into heartbeats and each of the heartbeats were transformed to 2D grayscale images which were input to CNN. In [44], two second segments of ECG signal are transformed to recurrence plot images to classify arrhythmia in two steps using deep learning model. In the first step the noise and ventricular fibrillation (VF) categories were recognized and in the second step, the atrial fibrillation (AF), normal, premature AF, and premature VF labels were classified.…”

Section: B Two-dimensional Cnn Approachesmentioning

confidence: 99%

ECG Heartbeat Classification Using Multimodal Fusion

et al. 2021

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Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the state-of-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN). In MFF, we extracted features from penultimate layer of CNNs and fused them to get unique and interdependent information necessary for better performance of classifier. These informational features are finally used to train a Support Vector Machine (SVM) classifier for ECG heart-beat classification. We demonstrate the superiority of the proposed fusion models by performing experiments on PhysioNet's MIT-BIH dataset for five distinct conditions of arrhythmias which are consistent with the AAMI EC57 protocols and on PTB diagnostics dataset for Myocardial Infarction (MI) classification. We achieved classification accuracy of 99.7% and 99.2% on arrhythmia and MI classification, respectively. Source code at https://github.com/zaamad/ECG-Heartbeat-Classification-Using-Multimodal-Fusion INDEX TERMS Convolutional neural network, deep learning, ECG, image fusion, multimodal fusion.

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“…It has categorized the signals using supervised machine learning approaches and outperformed the other methods based on other physiological signals. In [258], ECG arrhythmia classification has been done by using Convolutional Neural Networks and recurrence plots. The 1-D ECG data has been converted to 2-D recurrence plots and further utilized the classification of arrhythmias that has been validated using publicly available databases.…”

Section: ) Machine Learning and Deep Learning Techniques Based Classifiersmentioning

confidence: 99%

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Arora¹,

Mishra

2021

IEEE Access

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Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG and covers the most recent signal processing schemes with milestones over the last 150 years in a systematic manner. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing schemes and Complex Transformations are discussed. This paper summarizes the development of ECG features detection for cardiac anomalies and the history of the development of ECG monitors, beginning from String Galvanometer. It also discusses the automated detections on ECG beginning from 1960 to the most recent signal processing techniques. Additionally, this paper provides recommendations.

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ECG arrhythmia classification by using a recurrence plot and convolutional neural network

Cited by 137 publications

References 56 publications

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

ECG Heartbeat Classification Using Multimodal Fusion

Origins of ECG and Evolution of Automated DSP Techniques: A Review

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