Classification of Arrhythmia Using Artificial Neural Network with Grey Wolf Optimization

被引：0
|
作者：
Mohapatra, Saumendra Kumar [1 ]
Sahoo, Sipra [1 ]
Mohanty, Mihir Narayan [2 ]
机构：
[1] Siksha O Anusandhan Deemed Be Univ, Dept Comp Sci & Engn, ITER, Bhubaneswar, Odisha, India
[2] Siksha O Anusandhan Deemed Be Univ, Dept Elect & Commun Engn, ITER, Bhubaneswar, Odisha, India
来源：
BIOLOGICALLY INSPIRED TECHNIQUES IN MANY-CRITERIA DECISION MAKING | 2020年 / 10卷
关键词：
ECG; Arrhythmia; ANN; Optimization; GWO; ECG; DIAGNOSIS; PCA; ICA; LDA;
D O I：
10.1007/978-3-030-39033-4_1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Research on biomedical signal analysis is growing day-by-day. Accurate classification is an essential and challenging task. Authors in this work have tried to obtain better accuracy in the work cardiac signal classification using artificial neural network (ANN) classifier. Weights of the neural network are optimized using Grey Wolf Optimization (GWO) algorithm. The proposed optimized model is utilized for arrhythmia classification. Data collected from UCI repository for the said purpose. The performance is compared for both ANN and ANN with GWO. 93.38% classification accuracy is obtained by using ANN-GWO classifier. As compare to earlier work it is found that the proposed approach is much better in terms of accuracy.
引用
收藏
页码：3 / 10
页数：8
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