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

被引：23
|
作者：
Cui, Jianfeng [1 ]
Wang, Lixin [2 ]
He, Xiangmin [2 ]
De Albuquerque, Victor Hugo C. [3 ]
AlQahtani, Salman A. [4 ]
Hassan, Mohammad Mehedi [5 ]
机构：
[1] Xiamen Univ Technol, Sch Software Engn, Xiamen, Peoples R China
[2] Xiamen Univ Technol, Sch Comp & Informat Engn, Xiamen, Peoples R China
[3] Univ Porto FEUP, Fac Engn, Dept Mech Engn, Porto, Portugal
[4] King Saud Univ, Coll Comp & Informat Sci, Comp Engn Dept, Riyadh 11543, Saudi Arabia
[5] King Saud Univ, Coll Comp & Informat Sci, Informat Syst Dept, Riyadh 11543, Saudi Arabia
来源：
NEURAL COMPUTING & APPLICATIONS | 2023年 / 35卷 / 22期
基金：
中国博士后科学基金;
关键词：
ECG signals; Arrhythmia classification; Feature fusion; 1D-CNN4; NEURAL-NETWORK; INTERFERENCE; DIAGNOSIS; SYSTEM;
D O I：
10.1007/s00521-021-06487-5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：16073 / 16087
页数：15
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