通过对比特征增强提升帕金森病远程监测的抗噪能力
Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentation
- 将语音特征按值域分组构建对比对，训练鲁棒编码器
- 在多种噪声环境下，UPDRS预测误差显著降低
- 适合需高可靠性远程医疗评估的研究与应用
帕金森病（PD）是最常见的神经退行性疾病之一。远程监测作为新型评估方式，支持患者居家自测统一帕金森病评分量表（UPDRS）得分，提升了可及性。然而，测量过程中存在三类噪声：（1）患者自身引起的测量误差，（2）环境噪声，（3）传输中的数据包丢失，导致预测误差升高。为此，本文提出NoRo框架，实现噪声鲁棒的UPDRS预测。首先，将原始语音特征按某一选定特征的连续值划分为有序区间，构建对比对；其次，利用对比对训练多层感知机编码器，生成抗噪特征；最后，将该特征与原始特征拼接作为增强特征输入下游预测模型。此外，引入可定制的噪声注入评估方法。大量实验表明，NoRo在不同噪声环境和多种下游模型下均能有效提升UPDRS预测的鲁棒性。
原文摘要 · Abstract (English)
Parkinson's disease (PD) is one of the most common neurodegenerative disorder. PD telemonitoring emerges as a novel assessment modality enabling self-administered at-home tests of Unified Parkinson's Disease Rating Scale (UPDRS) scores, enhancing accessibility for PD patients. However, three types of noise would occur during measurements: (1) patient-induced measurement inaccuracies, (2) environmental noise, and (3) data packet loss during transmission, resulting in higher prediction errors. To address these challenges, NoRo, a noise-robust UPDRS prediction framework is proposed. First, the original speech features are grouped into ordered bins, based on the continuous values of a selected feature, to construct contrastive pairs. Second, the contrastive pairs are employed to train a multilayer perceptron encoder for generating noise-robust features. Finally, these features are concatenated with the original features as the augmented features, which are then fed into the UPDRS prediction models. Notably, we further introduces a novel evaluation approach with customizable noise injection module, and extensive experiments show that NoRo can successfully enhance the noise robustness of UPDRS prediction across various downstream prediction models under different noisy environments.
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