Abstract
Parkinson's disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson's severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson's disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson's Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson's disease or non-severe Parkinson's disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson's outperforming the state-of-the-art proposals.
Abstract (translated)
帕金森病在病情较晚的时候容易诊断，但在早期阶段很难诊断。早期的诊断对治疗症状非常重要。这种疾病会影响患者的日常生活，降低他们的生活质量，也是60岁以上人群中最常见的神经退行性疾病。目前，关于预测帕金森病严重程度的研究大部分都是在疾病进展到较晚阶段时进行的。在这项工作中，研究分析了一系列可以轻松从语音分析中提取的变量，使得这是一种非常非侵入性的技术。在这篇论文中，提出了一种基于不同深度学习技术的两种目的的方法。一方面，通过确定一个人是否患有严重或非严重的帕金森病，另一方面，通过回归分析方法确定患者疾病在一定程度上的发展程度。在考虑了运动和总标签的情况下，使用了统一帕金森病评分表（UPDRS），并且最佳结果是通过一种混合多层感知器（MLP）进行分类和回归得到的，输入数据的最重要特征使用自动编码器。在预测一个人是否患有严重帕金森病或非严重帕金森病的问题上，取得了99.15%的成功率。在疾病程度预测问题中，获得了0.15的均方误差（MSE）。使用完整的深度学习数据预处理和分类方法在帕金森病领域表现出了与现有最佳建议相媲美的效果。
URL
https://arxiv.org/abs/2402.05491
