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Automatic Recognition of Alzheimer's Disease Using Genetic Algorithms and Neural Network

机译：使用遗传算法和神经网络自动识别阿尔茨海默氏病

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摘要

We propose an Alzheimer's disease (AD) recognition method combined the genetic algorithms (GA) and the artificial neural network (ANN). Spontaneous EEG and auditory ERP data recorded from a single site in 16 early AD patients and 16 age-matched normal subjects were used. We made a feature pool including 88 spectral, 28 statistical and 2 nonlinear characteristics of EEG and 10 features of ERP. The combined GA/ANN was applied to find the dominant features automatically from the feature pool, and the selected features were used as a network input. The recognition rate of the ANN fed by this input was 81.9% for the untrained data set. These results lead to the conclusion that the combined GA/ANN approach may be useful for an early detection of the AD. This approach could be extended to a reliable classification system using EEG recording that can discriminate between groups.
机译：我们提出了一种结合遗传算法（GA）和人工神经网络（ANN）的阿尔茨海默氏病（AD）识别方法。使用16位早期AD患者和16位年龄相匹配的正常受试者从一个站点记录的自发性EEG和听觉ERP数据。我们建立了一个特征库，其中包括88个频谱，28个统计数据和2个EEG非线性特征以及10个ERP特征。应用组合的GA / ANN从特征库中自动查找主要特征，并将选定的特征用作网络输入。对于未经训练的数据集，此输入提供的人工神经网络的识别率为81.9％。这些结果得出结论：组合的GA / ANN方法可能对AD的早期检测有用。该方法可以扩展到使用EEG记录的可靠分类系统，该系统可以区分组。

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