Atrial fibrillation classification with artificial neural networks

被引：61
|
作者：
Kara, Sadik [1 ]
Okandan, Mustafa [1 ]
机构：
[1] Erciyes Univ, Dept Elect Engn, Biomed Res Grp, TR-38039 Kayseri, Turkey
来源：
PATTERN RECOGNITION | 2007年 / 40卷 / 11期
关键词：
electrocardiography; atrial fibrillation; artificial neural network; wavelet; Welch method; power spectral density;
D O I：
10.1016/j.patcog.2007.03.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The acquired 72 normal sinus rhythm ECGs and 80 ECGs with atrial fibrillation (AF) are decomposed with 'db 10' Daebauchies wavelets at level 6 and power spectral density was calculated for each decomposed signal with Welch method. Average power spectral density was calculated for six subbands and normalized to be used as input to the neural network. Levenberg-Marquart backpropagation feed forward neural network was built from logarithmic sigmoid transfer functions in three-layer form. The trained network was tested on 24 normal and 28 AF state ECGs. The classification performance was accomplished as 100% accurate. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2967 / 2973
页数：7
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