Deep learning holds tremendous potential in healthcare for uncovering hidden patterns within extensive clinical datasets, aiding in the diagnosis of various diseases. Parkinson's disease (PD) is a neurodegenerative condition characterized by the deterioration of brain function. In the initial stages of PD, automatic diagnosis poses a challenge due to the similarity in behavior between individuals with PD and those who are healthy. Our objective is to propose an effective model that can aid in the early detection of Parkinson's disease. We employed the VGRF gait signal dataset sourced from Physionet for distinguishing between healthy individuals and those diagnosed with Parkinson's disease. This paper introduces a novel deep learning architecture based on the LSTM network for automatically detecting freezing of gait episodes in Parkinson's disease patients. In contrast to conventional machine learning algorithms, this method eliminates manual feature engineering and proficiently captures prolonged temporal dependencies in gait patterns, thereby improving the diagnosis of Parkinson's disease. The LSTM network resolves the issue of vanishing gradients by employing memory blocks in place of self-connected hidden units, allowing for optimal information assimilation. To prevent overfitting, dropout and L2 regularization techniques have been employed. Additionally, the stochastic gradient-based optimizer Adam is used for the optimization process. The results indicate that our proposed approach surpasses current state-of-the-art models in FOG episode detection, achieving an accuracy of 97.71%, sensitivity of 99%, precision of 98%, and specificity of 96%. This demonstrates its potential as a superior classification method for Parkinson's disease detection.

翻译：深度学习在医疗健康领域具有巨大潜力，能够从大量临床数据中揭示隐藏模式，辅助多种疾病的诊断。帕金森病（PD）是一种以大脑功能退化为特征的神经退行性疾病。在PD初期阶段，由于患者与健康个体的行为表现相似，实现自动诊断面临挑战。我们的目标是提出一种能够辅助帕金森病早期检测的有效模型。我们采用了来自Physionet的VGRF步态信号数据集，用于区分健康个体与帕金森病确诊患者。本文提出了一种基于LSTM网络的新型深度学习架构，用于自动检测帕金森病患者的步态冻结发作事件。与传统的机器学习算法相比，该方法无需手动特征工程，并能有效捕捉步态模式中长期的时序依赖性，从而改善帕金森病的诊断。LSTM网络通过采用记忆块替代自连接的隐藏单元，解决了梯度消失问题，实现了信息的最优同化。为防止过拟合，采用了Dropout和L2正则化技术。此外，优化过程使用了基于随机梯度的Adam优化器。结果表明，我们提出的方法在FOG事件检测上超越了当前最先进的模型，达到了97.71%的准确率、99%的灵敏度、98%的精确度和96%的特异性。这证明了其作为一种优越的帕金森病检测分类方法的潜力。