Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

近年来，神经退行性疾病在老年人群中的发病率呈上升趋势。已经进行了大量研究来表征这些疾病。计算方法，特别是机器学习技术，现在是帮助和改进诊断以及疾病监测过程的非常有用的工具。在本文中，我们深入回顾了整个神经退行性疾病中使用的现有计算方法，即阿尔茨海默病、帕金森病和亨廷顿病、肌萎缩侧索硬化和多系统萎缩。我们提出了特定临床特征和现有计算方法的分类法。我们对每种疾病采用的各种模式和决策系统进行了详细分析。我们识别并呈现各种疾病中存在的睡眠障碍，这些睡眠障碍代表了发病检测的重要资产。我们概述了现有的数据集资源和评估指标。最后，我们确定当前剩余的开放挑战并讨论未来的前景。