ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms
ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms
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DOI：
10.1155/2018/4581272
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发表时间：
2018-01-01
影响因子：
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通讯作者：
Damasevicius, Robertas
中科院分区：
文献类型：
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作者：
Lauraitis, Andrius;Maskeliunas, Rytis;Damasevicius, Robertas
我们介绍了一种方法来预测恶化的反应状态的人有神经运动障碍，如手震颤和非自愿运动。这些不自主运动特征与患有亨廷顿病（HD）的患者中发生的症状密切相关。我们提出了一种混合（神经模糊）模型，结合人工神经网络（ANN）预测的功能能力水平（FCL）的人和一个模糊逻辑系统（FLS），以确定一个阶段的反应。我们使用智能手机或平板电脑分析了我们自己的3032条记录数据集，这些记录是从20名测试受试者（健康和HD患者）中收集的，要求患者在设备屏幕上定位圆形物体。我们描述的神经网络的数据的准备和标记，选择训练算法，模糊逻辑控制器的建模，以及混合模型的构建和实施。前馈反向传播（FFBP）神经网络实现的回归R值为0.98和均方误差（MSE）值为0.08，而FLS提供了一个最终的评价受试者的反应条件的FCL。
We introduce an approach to predict deterioration of reaction state for people having neurological movement disorders such as hand tremors and nonvoluntary movements. These involuntary motor features are closely related to the symptoms occurring in patients suffering from Huntington's disease (HD). We propose a hybrid (neurofuzzy) model that combines an artificial neural network (ANN) to predict the functional capacity level (FCL) of a person and a fuzzy logic system (FLS) to determine a stage of reaction. We analyzed our own dataset of 3032 records collected from 20 test subjects (both healthy and HD patients) using smart phones or tablets by asking a patient to locate circular objects on the device's screen. We describe the preparation and labelling of data for the neural network, selection of training algorithms, modelling of the fuzzy logic controller, and construction and implementation of the hybrid model. The feed-forward backpropagation (FFBP) neural network achieved the regression R value of 0.98 and mean squared error (MSE) values of 0.08, while the FLS provides a final evaluation of subject's reaction condition in terms of FCL.