Bio-signal Processing Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)

A. Ahmad, G. M. Khan
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引用次数: 12

Abstract

The aim of this paper is to explore the application of Neuro-Evolutionary Techniques to the diagnosis of various diseases. We applied the evolutionary technique of Cartesian Genetic programming Evolved Artificial Neural Network (CG-PANN) for the detection of three important diseases. Some cases showed excellent results while others are in the process of experimentation. In the first case we worked on diagnosing the extent of Parkinson's disease using a computer based test. Experiments in this case are in progress. In the second case, we applied the Fine Needle Aspirate (FNA) data for Breast Cancer from the WDBC website to our network to classify the samples as either benign (non-cancerous) or malignant (cancerous). The results from these experiments were highly satisfactory. In the third case, we developed a modified form of Pan-Tompkins's algorithm to detect the fiducial points from ECG signals and extracted key features from them. The features shall be applied to our network to classify the signals for the different types of Arrhythmias. Experimentation is still in progress.
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基于笛卡尔遗传规划进化人工神经网络(CGPANN)的生物信号处理
本文的目的是探讨神经进化技术在各种疾病诊断中的应用。我们将笛卡尔遗传规划进化人工神经网络(CG-PANN)的进化技术应用于三种重要疾病的检测。有些案例显示出优异的效果,而另一些则处于试验过程中。在第一个案例中,我们使用基于计算机的测试来诊断帕金森病的程度。这种情况下的实验正在进行中。在第二种情况下,我们将来自WDBC网站的乳腺癌细针抽吸(FNA)数据应用到我们的网络中,将样本分类为良性(非癌性)或恶性(癌性)。这些实验的结果非常令人满意。在第三种情况下,我们开发了一种改进的Pan-Tompkins算法,从心电信号中检测基点并从中提取关键特征。将这些特征应用到我们的网络中,对不同类型心律失常的信号进行分类。实验仍在进行中。
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