Li Xuemei, Zhou Tao, Qiu Shi
School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, China.
School of Computer Science and Engineering, North Minzu University, Yinchuan, China.
Front Aging Neurosci. 2022 May 10;14:888577. doi: 10.3389/fnagi.2022.888577. eCollection 2022.
Alzheimer's disease is a neurological disorder characterized by progressive cognitive dysfunction and behavioral impairment that occurs in old. Early diagnosis and treatment of Alzheimer's disease is great significance. Electroencephalography (EEG) signals can be used to detect Alzheimer's disease due to its non-invasive advantage. To solve the problem of insufficient analysis by single-channel EEG signal, we analyze the relationship between multiple channels and build PLV framework. To solve the problem of insufficient representation of 1D signal, a threshold-free recursive plot convolution network was constructed to realize 2D representation. To solve the problem of insufficient EEG signal characterization, a fusion algorithm of clinical features and imaging features was proposed to detect Alzheimer's disease. Experimental results show that the algorithm has good performance and robustness.
阿尔茨海默病是一种神经退行性疾病，其特征是老年人出现进行性认知功能障碍和行为损害。阿尔茨海默病的早期诊断和治疗具有重要意义。脑电图（EEG）信号因其非侵入性优势可用于检测阿尔茨海默病。为了解决单通道EEG信号分析不足的问题，我们分析了多个通道之间的关系并构建了PLV框架。为了解决一维信号表示不足的问题，构建了一种无阈值递归图卷积网络以实现二维表示。为了解决EEG信号特征不足的问题，提出了一种临床特征与影像特征融合算法来检测阿尔茨海默病。实验结果表明该算法具有良好的性能和鲁棒性。
