Abstract
Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally 1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 347 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.
Abstract (translated)
帕金森病（PD）是一种进行性神经退行性疾病，主要影响运动功能，并可能导致其晚期阶段出现轻度认知障碍（MCI）和痴呆。据《日本时报》和帕金森基金会的报告，全球约有1000万患者，患病率为每千人中有1.8例。早期且准确地诊断PD对于改善患者的预后至关重要。尽管许多研究已经利用机器学习（ML）和深度学习（DL）技术进行PD识别，但现有的综述往往在范围上有所局限，常常仅关注单一的数据模态，并未能捕捉到多模态方法的潜力。 为了填补这些空白，本研究对多种数据模态下的帕金森病识别系统进行了全面回顾。这包括磁共振成像（MRI）、基于步态的姿态分析、步态感觉数据、手写分析、语音测试数据、脑电图（EEG）以及多模态融合技术等。根据来自领先科学数据库的347篇以上文献，本综述探讨了数据收集方法、设置、特征表示及系统性能的关键方面，并重点关注识别准确性和鲁棒性。 此次调查旨在为研究人员提供一个全面资源，为其开发下一代帕金森病识别系统的行动指南。通过利用多样化的数据模态和尖端的机器学习范式，这项工作有助于推进PD诊断技术的进步，并通过创新、多模态的方法改善患者的护理质量。
URL
https://arxiv.org/abs/2505.00525
