# Graph Neural Networks for Parkinsons Disease Detection

arXiv: https://arxiv.org/abs/2409.07884 · HTML: https://mlsanity.com/paper/2409.07884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks](https://mlsanity.com/paper/1906.02174/) (2019)
- [Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech](https://mlsanity.com/paper/2309.14107/) (2023)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [Graph Multiview Canonical Correlation Analysis](https://mlsanity.com/paper/1811.12345/) (2018)
- [Compact Graph Architecture for Speech Emotion Recognition](https://mlsanity.com/paper/2008.02063/) (2020)
- [Experimental investigation on STFT phase representations for deep learning-based dysarthric speech detection](https://mlsanity.com/paper/2110.03283/) (2021)
- [Impact of Speech Mode in Automatic Pathological Speech Detection](https://mlsanity.com/paper/2406.09968/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities](https://mlsanity.com/paper/2505.00525/) (2025)

## Abstract (arXiv)

Despite the promising performance of state of the art approaches for Parkinsons Disease (PD) detection, these approaches often analyze individual speech segments in isolation, which can lead to suboptimal results. Dysarthric cues that characterize speech impairments from PD patients are expected to be related across segments from different speakers. Isolated segment analysis fails to exploit these inter segment relationships. Additionally, not all speech segments from PD patients exhibit clear dysarthric symptoms, introducing label noise that can negatively affect the performance and generalizability of current approaches. To address these challenges, we propose a novel PD detection framework utilizing Graph Convolutional Networks (GCNs). By representing speech segments as nodes and capturing the similarity between segments through edges, our GCN model facilitates the aggregation of dysarthric cues across the graph, effectively exploiting segment relationships and mitigating the impact of label noise. Experimental results demonstrate theadvantages of the proposed GCN model for PD detection and provide insights into its underlying mechanisms