Kashefpoor Masoud, Rabbani Hossein, Barekatain Majid
Department of Biomedical Engineering, Faculty of Advanced Medical Technologies, Isfahan University of Medical Sciences, Isfahan, Iran; Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Biomedical Engineering, Faculty of Advanced Medical Technologies, Isfahan University of Medical Sciences, Isfahan, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
J Med Signals Sens. 2016 Jan-Mar;6(1):25-32.
Alzheimer's disease (AD) is one of the most expensive and fatal diseases in the elderly population. Up to now, no cure have been found for AD, so early stage diagnosis is the only way to control it. Mild cognitive impairment (MCI) usually is the early stage of AD which is defined as decreasing in mental abilities such a cognition, memory, and speech not too severe to interfere daily activities. MCI diagnosis is rather hard and usually assumed as normal consequences of aging. This study proposes an accurate, mobile, and nonexpensive diagnostic approach based on electroencephalogram (EEG) signal. EEG signals were recorded using 19 electrodes positioned according to the 10-20 International system at resting eyes closed state from 16 normal and 11 MCI participants. Nineteen Spectral features are computed for each channel and examined using a correlation based algorithm to select the best discriminative features. Selected features are classified using a combination of neurofuzzy system and k-nearest neighbor classifier. Final results reach 88.89%, 100%, and 83.33% for accuracy, sensitivity, and specificity, respectively, which shows the potential of proposed method to be used as an MCI diagnostic tool, especially for screening a large population.
阿尔茨海默病（AD）是老年人群中最昂贵且致命的疾病之一。到目前为止，尚未发现治愈AD的方法，因此早期诊断是控制该病的唯一途径。轻度认知障碍（MCI）通常是AD的早期阶段，其被定义为诸如认知、记忆和言语等心理能力的下降，但程度不至于严重到干扰日常活动。MCI的诊断相当困难，通常被认为是衰老的正常后果。本研究提出了一种基于脑电图（EEG）信号的准确、便携且低成本的诊断方法。使用根据10-20国际系统定位的19个电极，在静息闭眼状态下记录了16名正常参与者和11名MCI参与者的EEG信号。为每个通道计算了19个频谱特征，并使用基于相关性的算法进行检验以选择最佳判别特征。使用神经模糊系统和k近邻分类器的组合对所选特征进行分类。最终结果的准确率、灵敏度和特异性分别达到88.89%、100%和83.33%，这表明所提出的方法有潜力用作MCI诊断工具，特别是用于对大量人群进行筛查。
