Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia

Feature extraction plays an important role in arrhythmia classification, and successful arrhythmia classification generally depends on ECG feature extraction. This paper proposed a feature extraction method combining traditional approaches and 1D-CNN aiming to find the optimal feature set to improve...

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Veröffentlicht in:Neural computing & applications Jg. 35; H. 22; S. 16073 - 16087
Hauptverfasser: Cui, Jianfeng, Wang, Lixin, He, Xiangmin, De Albuquerque, Victor Hugo C., AlQahtani, Salman A., Hassan, Mohammad Mehedi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.08.2023
Springer Nature B.V
Schlagworte:
Accuracy
Arrhythmia
Artificial Intelligence
Automatic classification
Cardiac arrhythmia
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Deep learning
Discrete Wavelet Transform
Discriminant analysis
Electrocardiography
Engineering
Feature extraction
Image Processing and Computer Vision
Information science
Machine learning
Morphology
Neural networks
Probability and Statistics in Computer Science
S.I.: AI-based e-diagnosis
Special Issue on Deep learning and big data analytics for medical e-diagnosis (AI-based e-diagnosis)
Support vector machines
Wavelet transforms
ECG signals
Arrhythmia classification
Feature fusion
1D-CNN4
ISSN:0941-0643, 1433-3058
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Zusammenfassung:Feature extraction plays an important role in arrhythmia classification, and successful arrhythmia classification generally depends on ECG feature extraction. This paper proposed a feature extraction method combining traditional approaches and 1D-CNN aiming to find the optimal feature set to improve the accuracy of arrhythmia classification. The proposed method is verified by using the MIT-BIH arrhythmia benchmark database. It is found that the features extracted by 1D-CNN and discrete wavelet transform form the optimal feature set with the average classification accuracy up to 98.35%, which is better than the latest methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-06487-5