Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise di...
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doaj-18122ad196f644a7bbd72211ee0c17862020-11-25T02:36:39ZengMDPI AGRemote Sensing2072-42922020-05-01121685168510.3390/rs12101685Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationAmin Ullah0Syed Muhammad Anwar1Muhammad Bilal2Raja Majid Mehmood3Software Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, PakistanSoftware Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, PakistanComputer and Electronics Systems Engineering, Hankuk University of Foreign Studies, Yongin-si 17035, KoreaInformation and Communication Technology Department, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang 43900, MalaysiaThe electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.https://www.mdpi.com/2072-4292/12/10/1685ECG signalclassificationarrhythmiaconvolution neural networkdeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amin Ullah Syed Muhammad Anwar Muhammad Bilal Raja Majid Mehmood |
spellingShingle |
Amin Ullah Syed Muhammad Anwar Muhammad Bilal Raja Majid Mehmood Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation Remote Sensing ECG signal classification arrhythmia convolution neural network deep learning |
author_facet |
Amin Ullah Syed Muhammad Anwar Muhammad Bilal Raja Majid Mehmood |
author_sort |
Amin Ullah |
title |
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation |
title_short |
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation |
title_full |
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation |
title_fullStr |
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation |
title_full_unstemmed |
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation |
title_sort |
classification of arrhythmia by using deep learning with 2-d ecg spectral image representation |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-05-01 |
description |
The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method. |
topic |
ECG signal classification arrhythmia convolution neural network deep learning |
url |
https://www.mdpi.com/2072-4292/12/10/1685 |
work_keys_str_mv |
AT aminullah classificationofarrhythmiabyusingdeeplearningwith2decgspectralimagerepresentation AT syedmuhammadanwar classificationofarrhythmiabyusingdeeplearningwith2decgspectralimagerepresentation AT muhammadbilal classificationofarrhythmiabyusingdeeplearningwith2decgspectralimagerepresentation AT rajamajidmehmood classificationofarrhythmiabyusingdeeplearningwith2decgspectralimagerepresentation |
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