用多尺度频域感知网络提升可穿戴设备对帕金森病的精准评估
Multi-scale Frequency-Aware Adversarial Network for Parkinson's Disease Assessment Using Wearable Sensors
- 结合医学先验知识进行频域分解，增强特征病理特异性
- 通过注意力多实例学习聚焦稀疏诊断片段，提升关键信号识别能力
- 在公开与私有数据集上均优于通用时间序列模型，适合临床辅助诊断
利用可穿戴传感器进行帕金森病（PD）严重程度评估为临床管理提供了客观依据。然而，通用时间序列模型在特征提取中缺乏病理特异性，难以捕捉与PD高度相关的细微信号。此外，PD症状的时间稀疏性导致关键诊断特征易被传统聚合方法稀释，进一步增加评估难度。为此，我们提出多尺度频域感知对抗多实例网络（MFAM）。该模型通过基于医学先验知识的频域分解模块提升特征特异性，并引入基于注意力的多实例学习（MIL）框架，自适应聚焦最具诊断价值的稀疏片段。我们在公开的PADS数据集（用于PD与鉴别诊断二分类）和一个私有数据集（用于四分类严重程度评估）上全面验证了MFAM。实验结果表明，MFAM在处理具有病理特异性的复杂临床时间序列方面显著优于通用时间序列模型，为帕金森病严重程度的自动化评估提供了有前景的解决方案。
原文摘要 · Abstract (English)
Severity assessment of Parkinson's disease (PD) using wearable sensors offers an effective, objective basis for clinical management. However, general-purpose time series models often lack pathological specificity in feature extraction, making it difficult to capture subtle signals highly correlated with PD.Furthermore, the temporal sparsity of PD symptoms causes key diagnostic features to be easily "diluted" by traditional aggregation methods, further complicating assessment. To address these issues, we propose the Multi-scale Frequency-Aware Adversarial Multi-Instance Network (MFAM). This model enhances feature specificity through a frequency decomposition module guided by medical prior knowledge. Furthermore, by introducing an attention-based multi-instance learning (MIL) framework, the model can adaptively focus on the most diagnostically valuable sparse segments.We comprehensively validated MFAM on both the public PADS dataset for PD versus differential diagnosis (DD) binary classification and a private dataset for four-class severity assessment. Experimental results demonstrate that MFAM outperforms general-purpose time series models in handling complex clinical time series with specificity, providing a promising solution for automated assessment of PD severity.
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