EEG-Based Discrimination Between Patients with MCI and Alzheimer

B. Kazimipour, R. Boostani, A. Borhani-Haghighi, S. Almatarneh, Mohammad Aljaidi
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引用次数: 1

Abstract

There is a high similarity between the signs and symptoms of patients with Alzheimer and those with mild cognitive impairment (MCI). Although several attempts have been made to differentiate these two groups of patients by decoding the fluctuation of their electroencephalogram (EEG), the achieved results are not yet promising. To increase the differentiation rate, in this study, 14 patients with Alzheimer from 13 patients with MCI have been voluntarily enrolled while their EEG signals are recorded in presence of visual stimuli. To suppress the disrupting artifacts and noises (e.g., eye-blink and movement artefact) from the recorded EEGs, independent component analysis is applied. Next, the visual evoke potential (VEP) patterns are extracted by synchronous averaging and then multi-linear principal component analysis (MPCA) is applied to elicit discriminative features from VEPs of the patients. After feature extraction by MPCA, the reduced feature vectors of both groups are applied to a nearest neighbor classifier, leading to 77.35% differentiation accuracy.
基于脑电图的轻度认知损伤与阿尔茨海默病患者的鉴别
阿尔茨海默病患者的体征和症状与轻度认知障碍(MCI)患者有很高的相似性。虽然已经进行了几次尝试,通过解码脑电图(EEG)的波动来区分这两组患者,但取得的结果尚不乐观。为了提高分化率,本研究从13名MCI患者中自愿招募了14名阿尔茨海默病患者,并在视觉刺激下记录了他们的脑电图信号。为了抑制记录的脑电图中的干扰伪影和噪声(如眨眼和运动伪影),应用了独立分量分析。其次,采用同步平均的方法提取视觉诱发电位(VEP)模式,然后采用多线性主成分分析(MPCA)方法提取患者VEP的判别特征;经过MPCA特征提取后,将两组的约简特征向量应用到最近邻分类器中,分类准确率达到77.35%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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