深度学习在阿尔兹海默病患者脑电图分析中的应用综述

A review of deep learning in EEG analysis of patients with alzheimer

阿尔兹海默病正在成为危害65岁及以上老年人身体健康的高发病症,该疾病的早期诊断对于预防和治疗都尤为重要。脑电图能够反映大脑的神经电生理活动,被广泛应用于老年科临床检测,具有经济、实时、动态和安全等优点。深度学习算法能够挖掘临床数据(例如脑电图)中隐含的丰富信息,成为计算机辅助诊断领域的研究热点。本文将综述基于脑电图的深度学习算法在阿尔兹海默病患者的识别和分类上的应用,对不同的算法和思路进行了比较和总结,讨论深度学习在AD临床应用中存在的问题和挑战,对未来研究方向进行了展望,这将对提高AD疾病早期诊断和预测的准确性具有重要意义。

Alzheimer’s disease is becoming a highly prevalent disease that endangers the physical health of elderly people aged 65 years and above,and early diagnosis of this disease is particularly important for both prevention and treatment.EEG reflects the neurophysiological activity of the brain and is widely used in geriatric clinical testing,which has the advantages of being economical,real-time,dynamic and safe.Deep learning algorithms are capable of mining the rich information implied in clinical data(e.g.,EEG)and have become a trending research topic in the field of computer-aided diagnosis.In this paper,we will review the application of deep learning algorithms for early identification and classification of Alzheimer’s disease,compare and summarize different algorithms and ideas,discuss the problems and challenges of deep learning in clinical applications of AD,and provide an outlook on future research directions,which will be important for improving the accuracy of early diagnosis and prediction of AD.