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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Vydané v:	Neural computing & applications Ročník 35; číslo 22; s. 16073 - 16087
Hlavní autori:	Cui, Jianfeng, Wang, Lixin, He, Xiangmin, De Albuquerque, Victor Hugo C., AlQahtani, Salman A., Hassan, Mohammad Mehedi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.08.2023 Springer Nature B.V
Predmet:	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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