用于早期识别阿尔茨海默病认知障碍的埃尔曼神经网络
Elman neural network for the early identification of cognitive impairment in Alzheimer's disease.
作者信息
Bertè Francesco, Lamponi Giuseppe, Calabrò Rocco Salvatore, Bramanti Placido
出版信息
Funct Neurol. 2014 Jan-Mar;29(1):57-65.
Abstract
Early detection of dementia can be useful to delay progression of the disease and to raise awareness of the condition. Alterations in temporal and spatial EEG markers have been found in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Herein, we propose an automatic recognition method of cognitive impairment evaluation based on EEG analysis using an artificial neural network (ANN) combined with a genetic algorithm (GA). The EEGs of 43 AD and MCI patients (aged between 62 and 88 years) were recorded, analyzed and correlated with their MMSE scores. Quantitative EEGs were calculated using discrete wavelet transform. The data obtained were analyzed by the means of the combined use of ANN and GA to determine the degree of cognitive impairment. The good recognition rate of ANN fed with these inputs suggests that the combined GA/ANN approach may be useful for early detection of AD and could be a valuable tool to support physicians in clinical practice.
摘要
早期发现痴呆症有助于延缓疾病进展并提高对该病症的认识。在阿尔茨海默病（AD）和轻度认知障碍（MCI）患者中发现了颞叶和空间脑电图标记的改变。在此，我们提出一种基于脑电图分析的认知障碍评估自动识别方法，该方法使用人工神经网络（ANN）结合遗传算法（GA）。记录了43例AD和MCI患者（年龄在62至88岁之间）的脑电图，进行分析并将其与他们的简易精神状态检查表（MMSE）评分相关联。使用离散小波变换计算定量脑电图。通过联合使用ANN和GA对获得的数据进行分析，以确定认知障碍的程度。用这些输入数据训练的ANN具有良好的识别率，这表明GA/ANN联合方法可能有助于AD的早期检测，并且可能成为临床实践中支持医生的有价值工具。
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