Summary of Graph Neural Networks For Parkinsons Disease Detection, by Shakeel A. Sheikh and Yacouba Kaloga and Md Sahidullah and Ina Kodrasi
Graph Neural Networks for Parkinsons Disease Detection
by Shakeel A. Sheikh, Yacouba Kaloga, Md Sahidullah, Ina Kodrasi
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach uses Graph Convolutional Networks (GCNs) to detect Parkinson’s Disease (PD) based on speech patterns, addressing limitations in current methods that analyze individual segments in isolation. The framework represents speech segments as nodes and captures relationships between them through edges, enabling the aggregation of dysarthric cues across the graph. This mitigates label noise and improves PD detection performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Parkinson’s Disease affects how people speak, making it hard to detect. Current methods look at one part of a speech at a time, but this can be wrong. New research proposes using special computer networks to understand how different parts of a speech are connected. This helps the system learn from patterns in how people with PD talk. The results show that this new approach is better than current ones for detecting Parkinson’s Disease. |
