Classification of heart abnormalities using artificial neural network

被引：0
|
作者：
Saad, Mohd Hanif Md [1 ]
Nor, Mohd Jailani Mohd [1 ]
Bustami, Fadzlul Rahimi Ahmad [1 ]
Ngadiran, Ruzelita [1 ]
机构：
[1] MEMS in Automotives Application Research Group, Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, Selangor, Malaysia
来源：
Journal of Applied Sciences | 2007年 / 7卷 / 06期
关键词：
Artificial heart - Neural networks - Image processing - Backpropagation;
D O I：
10.3923/jas.2007.820.825
中图分类号：
学科分类号：
摘要：
This paper describes heart abnormalities classification procedures utilising features obtained from Time-Frequency Spectogram and Image Processing Techniques. Enhanced spatial features of time-frequency spectrogram were extracted and fed into a Multi-Layer, Back-Propagation trained Artificial Neural Network and the corresponding abnormalities were classified. A confidence factor is calculated for every classification result indicating the degree of belief that the classification is true. It was observed that the classification method was able to give 100% correct classification based on features that was extracted from the training data sets and the validation data sets. © 2007 Asian Network for Scientific Information.
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页码：820 / 825
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