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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Deep Learning-Aided Parkinson's Disease Diagnosis from Handwritten Dynamics
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Deep Learning-Aided Parkinson's Disease Diagnosis from Handwritten Dynamics

机译：手写动力学的深度学习辅助帕金森氏病诊断

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Parkinson's Disease (PD) automatic identification in early stages is one of the most challenging medicine-related tasks to date, since a patient may have a similar behaviour to that of a healthy individual at the very early stage of the disease. In this work, we cope with PD automatic identification by means of a Convolutional Neural Network (CNN), which aims at learning features from a signal extracted during the individual's exam by means of a smart pen composed of a series of sensors that can extract information from handwritten dynamics. We have shown CNNs are able to learn relevant information, thus outperforming results obtained from raw data. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster PD-related research.
机译：迄今为止，帕金森氏病（PD）的自动识别是迄今为止与医学相关的最具挑战性的任务之一，因为患者在疾病的早期可能会表现出与健康个体相似的行为。在这项工作中，我们通过卷积神经网络（CNN）来应对PD自动识别，该网络的目的是通过由一系列可以提取信息的传感器组成的智能笔，从个人考试期间提取的信号中学习特征。从手写动态。我们已经显示了CNN能够学习相关信息，因此胜过了从原始数据获得的结果。此外，这项工作旨在建立一个公共数据集，以供全世界的研究人员使用，以促进与PD相关的研究。

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