Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia
| Autor: | Salman A. AlQahtani, Lixin Wang, Victor Hugo C. de Albuquerque, Mohammad Mehedi Hassan, Jianfeng Cui, Xiangmin He |
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| Rok vydání: | 2021 |
| Předmět: |
Discrete wavelet transform
Feature fusion business.industry Computer science Deep learning Feature extraction Benchmark database Pattern recognition ComputingMethodologies_PATTERNRECOGNITION ECG feature Artificial Intelligence Computational Science and Engineering ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS Artificial intelligence business Feature set Software |
| Zdroj: | Neural Computing and Applications. |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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