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Characterizing Parkinson's Disease from Speech Samples Using Deep Structured Learning

机译：使用深层结构学习，从语音样本中表征帕金森病

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摘要

An early detection of neurodegenerative diseases, such as Parkinson's disease, can improve therapy effectiveness and, by consequence, the patient's quality of life. This paper proposes a new methodology for automatic classification of voice samples regarding the presence of acoustic patterns of Parkinson's disease, using a deep structured neural network. This is a low cost non-invasive approach that can raise alerts in a pre-clinical stage. Aiming to a higher diagnostic detail, it is also an objective to accurately estimate the stage of evolution of the disease allowing to understand in what extent the symptoms have developed. Therefore, two types of classification problems are explored: binary classification and multiclass classification. For binary classification, a deep structured neural network was developed, capable of correctly diagnosing 93.4% of cases. For the multiclass classification scenario, in addition to the deep neural network, a K-nearest neighbour algorithm was also used to establish a reference for comparison purposes, while using a common database. In both cases the original feature set was optimized using principal component analysis and the results showed that the proposed deep structure neural network was able to provide more accurate estimations about the disease's stage, reaching a score of 84.7%. The obtained results are promising and create the motivation to further explore the model's flexibility and to pursue better results.
机译：早期发现神经变性疾病，如帕金森病，可以提高治疗效果，并因此，患者的生活质量。本文采用深层结构性神经网络，提出了一种关于帕金森病的声学模式的语音样本的自动分类的新方法。这是一种低成本的非侵入性方法，可以在临床前阶段提高警报。旨在更高的诊断细节，它也是准确估计疾病演化阶段的目的，允许在症状发展的程度上理解。因此，探索了两种类型的分类问题：二进制分类和多字母分类。对于二进制分类，开发了深度结构的神经网络，能够正确诊断93.4％的病例。对于多字母分类方案，除了深神经网络之外，还用于建立比较目的的基准，同时使用公共数据库来建立参考。在这两种情况下，使用主成分分析优化了原始功能集，结果表明，建议的深度结构神经网络能够为疾病阶段提供更准确的估计，得分为84.7％。获得的结果是有前途的，创造了进一步探索模型灵活性并追求更好结果的动力。

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