Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913685972189184 |
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| author | Laouedj, Sarah Wang, Yuzhe Villalba, Jesus Thebaud, Thomas Moro-Velazquez, Laureano Dehak, Najim |
| author_facet | Laouedj, Sarah Wang, Yuzhe Villalba, Jesus Thebaud, Thomas Moro-Velazquez, Laureano Dehak, Najim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings Laouedj, Sarah Wang, Yuzhe Villalba, Jesus Thebaud, Thomas Moro-Velazquez, Laureano Dehak, Najim Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.07025 |