Abstract
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.
Abstract (translated)
在这项研究中，我们探索了使用频谱图来表示手写信号以评估神经退行性疾病的方法。参与本研究的有42名健康对照组（CTL），35名帕金森病患者（PD），21名阿尔茨海默病患者（AD）和15名帕金森病模拟病例（PDM）。我们使用了卷积神经网络（CNN）和结合了长短时记忆层的卷积神经网络（CNN-BLSTM）模型，采用多通道固定大小频谱图及基于帧的频谱图进行二元分类。我们的结果表明，手写任务以及频谱图信道组合对分类性能有显著影响。在AD vs. CTL 的分类中取得了最高的F1分数（89.8%），PD vs. CTL 分类得分为74.5%，而 PD vs. PDM 的分类得分则为77.97%。CNN模型始终优于CNN-BLSTM模型。 我们测试了不同的滑动窗口长度，以构建基于帧的频谱图。对于AD病例，1秒的窗口表现最佳；而对于PD病例，较长的窗口可以改善其分类效果；至于 PD vs. PDM 的分类任务，则窗口长度对结果影响不大。
URL
https://arxiv.org/abs/2502.07025
