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

• DOI: 10.1007/s00521-021-06487-5
• Publication Date: 2021-09-09T18:02:36Z
• Authors: Jianfeng Cui; Lixin Wang; Xiangmin He; Victor Hugo C. De...; Salman A. AlQahtani; Mohammad Mehedi H...
• Keywords: Benchmark (surveying); Feature (linguistics); Cardiac 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 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.