Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias
Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/16/2874 |
| _version_ | 1827599219049889792 |
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| author | Bing Zhang Jizhong Liu |
| author_facet | Bing Zhang Jizhong Liu |
| author_sort | Bing Zhang |
| collection | DOAJ |
| description | Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called “discriminative sparse-code error” into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition. |
| first_indexed | 2024-03-09T04:08:39Z |
| format | Article |
| id | doaj.art-608f4e93e2a94437a14964b187671cb7 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T04:08:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-608f4e93e2a94437a14964b187671cb72023-12-03T14:03:08ZengMDPI AGMathematics2227-73902022-08-011016287410.3390/math10162874Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac ArrhythmiasBing Zhang0Jizhong Liu1Nanchang Key Laboratory of Medical and Technology Research, School of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaNanchang Key Laboratory of Medical and Technology Research, School of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaElectrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called “discriminative sparse-code error” into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition.https://www.mdpi.com/2227-7390/10/16/2874electrocardiogram signaldiscriminative convolutional sparse codingdictionary filter learninglinear SVM |
| spellingShingle | Bing Zhang Jizhong Liu Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias Mathematics electrocardiogram signal discriminative convolutional sparse coding dictionary filter learning linear SVM |
| title | Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias |
| title_full | Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias |
| title_fullStr | Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias |
| title_full_unstemmed | Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias |
| title_short | Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias |
| title_sort | discriminative convolutional sparse coding of ecg signals for automated recognition of cardiac arrhythmias |
| topic | electrocardiogram signal discriminative convolutional sparse coding dictionary filter learning linear SVM |
| url | https://www.mdpi.com/2227-7390/10/16/2874 |
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