利用机器学习检测帕金森病

N. R, Vinora A, S AlwynRajiv, Ajitha E, Sivakarthi G
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引用次数: 0

摘要

下丘脑是中脑的一部分,是目前无法治愈的帕金森病(PD)中多巴胺能神经元丢失的主要部位(中脑)。所有PD药物都是针对症状的;它们不能阻止或延缓多巴胺能神经元的退化。机器学习技术在这里是适用的,因为震颤检测和分类对于PD患者的诊断和治疗至关重要。这是临床实践中最常见的运动障碍之一,通常根据病因或行为特征进行分类。另一个关键的挑战是识别和评估PD相关的步态模式,如步态开始和冻结,这是PD症状的定义。考虑到90%的帕金森氏症患者有声音障碍,分析语言数据以区分健康个体和患有帕金森氏症的人是至关重要的。这项研究受到正在进行的PD管理倡议的启发,对当前最先进的技术和未来研究的一些潜力进行了简要的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Parkinson's Disease using Machine Learning
SubstantiaNigra, a part of the Mesencephalon, is the primary site of dopaminergic neuron loss in Parkinson's disease (PD), which is currently incurable (midbrain). All PD medications address symptoms; they do not prevent or postpone the degeneration of dopaminergic neurons. Machine learning techniques are applicable here since tremor detection and classification are crucial for the diagnosis and treatment of PD patients. This is one of the most common movement disorders seen in clinical practice, and it is frequently categorized according to etiological or behavioral traits. Another critical challenge is identifying and evaluating PD-related gait patterns, such as gait start and freezing, which are defining PD symptoms. Given that 90% of Parkinson's disease patients have vocal impairment, it is crucial to analyze speech data to discriminate between healthy individuals and those with the condition. This study, which presents a succinct assessment of the current state-of-the-art and some potential for future research, was inspired by the ongoing PD management initiative.
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