An electrocardiogram signal classification using a hybrid machine learning and deep learning approach
An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442524000686 |
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| author | Faramarz Zabihi Fatemeh Safara Behrouz Ahadzadeh |
| author_facet | Faramarz Zabihi Fatemeh Safara Behrouz Ahadzadeh |
| author_sort | Faramarz Zabihi |
| collection | DOAJ |
| description | An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection. |
| format | Article |
| id | doaj-art-e72d2ff4e56441b3aba1762efcd04db3 |
| institution | Kabale University |
| issn | 2772-4425 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-e72d2ff4e56441b3aba1762efcd04db32024-12-19T11:02:44ZengElsevierHealthcare Analytics2772-44252024-12-016100366An electrocardiogram signal classification using a hybrid machine learning and deep learning approachFaramarz Zabihi0Fatemeh Safara1Behrouz Ahadzadeh2Department of Electrical, Computer and IT Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran; Corresponding author.Department of Computer Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, IranDepartment of Electrical, Computer and IT Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranAn electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection.http://www.sciencedirect.com/science/article/pii/S2772442524000686Cardiac arrhythmia detectionDeep learningResidual neural networkElectrocardiogram signalRandom forest |
| spellingShingle | Faramarz Zabihi Fatemeh Safara Behrouz Ahadzadeh An electrocardiogram signal classification using a hybrid machine learning and deep learning approach Healthcare Analytics Cardiac arrhythmia detection Deep learning Residual neural network Electrocardiogram signal Random forest |
| title | An electrocardiogram signal classification using a hybrid machine learning and deep learning approach |
| title_full | An electrocardiogram signal classification using a hybrid machine learning and deep learning approach |
| title_fullStr | An electrocardiogram signal classification using a hybrid machine learning and deep learning approach |
| title_full_unstemmed | An electrocardiogram signal classification using a hybrid machine learning and deep learning approach |
| title_short | An electrocardiogram signal classification using a hybrid machine learning and deep learning approach |
| title_sort | electrocardiogram signal classification using a hybrid machine learning and deep learning approach |
| topic | Cardiac arrhythmia detection Deep learning Residual neural network Electrocardiogram signal Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2772442524000686 |
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