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首页> 外文期刊>International Journal of Innovative Computing Information and Control >CLASSIFICATION AND MEDICAL DIAGNOSIS OF SCALP EEG USING ARTIFICIAL NEURAL NETWORKS
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CLASSIFICATION AND MEDICAL DIAGNOSIS OF SCALP EEG USING ARTIFICIAL NEURAL NETWORKS

机译：人工神经网络对头皮脑电的分类和医学诊断

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摘要

An automatic Artificial Neural Network-Aided Diagnosis (ANNAD) system is designed in this study for initial scalp EEG screening to establish whether a given subject is epileptic or not. A unique ANNAD-based decision-making process is devised to make this distinction by 1) computing all standard EEG parameters in both time and frequency domain and 2) determining which of these parameters will yield the optimal classifier. The temporal parameters include activity, mobility and complexity, and the frequency parameters include the spectral power in delta, theta, alpha, beta I and II, and gamma. A single layer perceptron was used to conduct this analysis without initially setting any conditions on the weight vector, but rather allowed for the random generation of these conditions, with as many trials as necessary. This is an important first step that confines the search space to only those EEG data that have a very high likelihood of being recorded from epileptic patients, significantly minimizing the time for accurate diagnosis. We have evaluated our system using 125 EEG files selected randomly from a database consisting of 10 subjects (5 non-epileptic and 5 epileptic). The proposed ANNAD system was capable of diagnosing subjects with epilepsy with an accuracy of 92.04% and a calculated F-measure of 93.39%.
机译：在这项研究中，设计了一个自动人工神经网络辅助诊断（ANNAD）系统，用于初始头皮脑电图筛查，以确定给定的受试者是否患有癫痫。设计了一种独特的基于ANNAD的决策流程，以通过以下方式进行区分：1）在时域和频域中计算所有标准EEG参数，以及2）确定这些参数中的哪一个将产生最佳分类器。时间参数包括活动性，迁移率和复杂性，而频率参数包括以δ，θ，α，βI和II和γ表示的谱功率。使用单层感知器进行此分析时，首先无需在权重矢量上设置任何条件，而是允许根据需要的多次试验随机生成这些条件。这是重要的第一步，它将搜索空间限制为仅包含那些极有可能从癫痫患者那里记录到的EEG数据，从而极大地缩短了准确诊断的时间。我们使用125个EEG文件评估了我们的系统，这些文件是从10名受试者（5名非癫痫病患者和5名癫痫病患者）组成的数据库中随机选择的。所提出的ANNAD系统能够诊断出癫痫病患，其准确率达到92.04％，计算出的F值达到93.39％。

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    Mercedes Cabrerizo; Melvin Ayala; Prasanna Jayakar; malek adjouadi;

  • 作者单位

    Center for Advance Technology and Education (CATE) College of Engineering and Computing Florida International University 10555 West Flagler Street, Miami, FL 33174, USA,Department of Brain Institute Miami Children's Hospital 3100 South West 62nd Avenue, Miami, FL 33155, USA;

    Center for Advance Technology and Education (CATE) College of Engineering and Computing Florida International University 10555 West Flagler Street, Miami, FL 33174, USA;

    Department of Brain Institute Miami Children's Hospital 3100 South West 62nd Avenue, Miami, FL 33155, USA;

    Center for Advance Technology and Education (CATE) College of Engineering and Computing Florida International University 10555 West Flagler Street, Miami, FL 33174, USA;

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  • 正文语种 eng
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  • 关键词

    feature extraction; artificial neural networks; epileptic vs. non-epileptic EEG classification;

    机译：特征提取;人工神经网络;癫痫与非癫痫性脑电分类;

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