Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
531721 | Pattern Recognition | 2007 | 7 Pages |
Abstract
The acquired 72 normal sinus rhythm ECGs and 80 ECGs with atrial fibrillation (AF) are decomposed with ‘db10’ 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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Sadik Kara, Mustafa Okandan,