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.

引用

页码：820 / 825

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