Aslan Umut, Akşahin Mehmet Feyzi
Department of Electrical and Electronic Engineering, Gazi University, Ankara, Turkey.
Biomed Eng Online. 2025 Apr 25;24(1):47. doi: 10.1186/s12938-025-01369-6.
Alzheimer's disease (AD) is characterized by deficits in cognition, behavior, and intellectual functioning, and Mild Cognitive Impairment (MCI) refers to individuals whose cognitive impairment deviates from what is expected for their age but does not significantly interfere with daily activities. Because there is no treatment for AD, early prediction of AD can be helpful to reducing the progression of this disease. This study examines the Electroencephalography (EEG) signal of 3 distinct groups, including AD, MCI, and healthy individuals. Recognizing the non-stationary nature of EEG signals, two nonlinear approaches, Poincare and Entropy, are employed for meaningful feature extraction. Data should be segmented into epochs to extract features from EEG signals, and feature extraction approaches should be implemented for each one. The obtained features are given to machine learning algorithms to classify the subjects. Extensive experiments were conducted to analyze the features comprehensively. The results demonstrate that our proposed method surpasses previous studies in terms of accuracy, sensitivity, and specificity, indicating its effectiveness in classifying individuals with AD, MCI, and those without cognitive impairment.
阿尔茨海默病（AD）的特征是认知、行为和智力功能存在缺陷，而轻度认知障碍（MCI）是指认知障碍偏离其年龄预期但不会显著干扰日常活动的个体。由于目前尚无针对AD的治疗方法，对AD进行早期预测有助于减缓该疾病的进展。本研究检测了包括AD患者、MCI患者和健康个体在内的3个不同组别的脑电图（EEG）信号。鉴于EEG信号具有非平稳性，采用了庞加莱和熵这两种非线性方法进行有意义的特征提取。应将数据分割成时间段，以便从EEG信号中提取特征，并且应为每个时间段实施特征提取方法。将获得的特征输入机器学习算法以对受试者进行分类。进行了大量实验以全面分析这些特征。结果表明，我们提出的方法在准确性、敏感性和特异性方面优于先前的研究，表明其在对AD患者、MCI患者和无认知障碍者进行分类方面是有效的。
