Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings
This work addresses early diagnosis of neurodegenerative diseases for patients, but it is incremental as it applies existing CNN and CNN-BLSTM models to new data with specific optimizations.
The study tackled detecting neurodegenerative diseases like Alzheimer's and Parkinson's using handwriting spectrograms, achieving an F1-score of 89.8% for Alzheimer's vs. healthy controls and 74.5% for Parkinson's vs. healthy controls.
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.