用注意力增强LSTM，通过语音信号早筛帕金森病
Enhanced LSTM by Attention Mechanism for Early Detection of Parkinson's Disease through Voice Signals
- 用注意力机制改进LSTM，捕捉语音时序中的关键特征
- 在UCI数据集上预测UPDRS评分，准确率显著提升
- 适合医疗AI、早期神经退行性疾病筛查研究者
帕金森病（PD）是一种以运动与非运动症状为特征的神经退行性疾病。统一帕金森病评定量表（UPDRS）是评估病情严重程度的重要工具。本文提出一种完整方法，利用改进的长短期记忆网络（LSTM）结合注意力机制、数据增强和特征选择技术，预测UPDRS评分。实验数据来自加州大学欧文分校机器学习库，包含早期帕金森病患者的多种语音指标。采用递归特征消除（RFE）进行高效特征筛选，并通过抖动（jittering）扩充数据集。精心设计的LSTM网络能有效捕捉数据中的时间动态变化，结合注意力机制后进一步提升了对序列重要性的识别能力。该方法为帕金森病医疗数据的精准、个性化分析提供了可行路径。
原文摘要 · Abstract (English)
Parkinson's disease (PD) is a neurodegenerative condition characterized by notable motor and non-motor manifestations. The assessment tool known as the Unified Parkinson's Disease Rating Scale (UPDRS) plays a crucial role in evaluating the extent of symptomatology associated with Parkinson's Disease (PD). This research presents a complete approach for predicting UPDRS scores using sophisticated Long Short-Term Memory (LSTM) networks that are improved using attention mechanisms, data augmentation techniques, and robust feature selection. The data utilized in this work was obtained from the UC Irvine Machine Learning repository. It encompasses a range of speech metrics collected from patients in the early stages of Parkinson's disease. Recursive Feature Elimination (RFE) was utilized to achieve efficient feature selection, while the application of jittering enhanced the dataset. The Long Short-Term Memory (LSTM) network was carefully crafted to capture temporal fluctuations within the dataset effectively. Additionally, it was enhanced by integrating an attention mechanism, which enhances the network's ability to recognize sequence importance. The methodology that has been described presents a potentially practical approach for conducting a more precise and individualized analysis of medical data related to Parkinson's disease.
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