用手写信号的频谱图检测神经退行性疾病，准确率最高达89.8%。
Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings
- 将手写轨迹转为频谱图，用卷积网络分类疾病
- 阿尔茨海默病区分准确率最高，达89.8%
- 1秒窗口最适合阿尔茨海默病，更长窗口利于帕金森
本研究探索了用频谱图表示手写信号以评估神经退行性疾病的方法，涵盖42名健康对照（CTL）、35名帕金森病（PD）患者、21名阿尔茨海默病（AD）患者及15名帕金森病模拟者（PDM）。采用CNN与CNN-BLSTM模型，基于多通道固定尺寸和帧级频谱图进行二分类。结果表明，手写任务类型与频谱图通道组合显著影响分类性能。在AD vs. CTL中达到最高F1-score（89.8%），PD vs. CTL为74.5%，PD vs. PDM为77.97%。CNN整体优于CNN-BLSTM。测试了不同滑动窗长度构建帧级频谱图：1秒窗对AD最优，更长窗口提升PD分类效果，而对PD vs. PDM影响较小。
原文摘要 · Abstract (English)
In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease (AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and CNN-BLSTM models for binary classification using both multi-channel fixed-size and frame-based spectrograms. Our results showed that handwriting tasks and spectrogram channel combinations significantly impacted classification performance. The highest F1-score (89.8%) was achieved for AD vs. CTL, while PD vs. CTL reached 74.5%, and PD vs. PDM scored 77.97%. CNN consistently outperformed CNN-BLSTM. Different sliding window lengths were tested for constructing frame-based spectrograms. A 1-second window worked best for AD, longer windows improved PD classification, and window length had little effect on PD vs. PDM.
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