Surface EMG Profiling in Parkinson's Disease: Advancing Severity Assessment with GCN-SVM

• URL: http://arxiv.org/abs/2507.14153v1
• Date: Fri, 04 Jul 2025 00:29:31 GMT
• Title: Surface EMG Profiling in Parkinson's Disease: Advancing Severity Assessment with GCN-SVM
• Authors: Daniel Cieślak, Barbara Szyca, Weronika Bajko, Liwia Florkiewicz, Kinga Grzęda, Mariusz Kaczmarek, Helena Kamieniecka, Hubert Lis, Weronika Matwiejuk, Anna Prus, Michalina Razik, Inga Rozumowicz, Wiktoria Ziembakowska,
• Abstract summary: This study introduces a novel approach utilizing surface electromyography (sEMG) to objectively assess Parkinson's disease severity.<n>Initial analysis of sEMG data from five PD patients and five healthy controls revealed significant neuromuscular differences.
• Score: 0.15557122832359727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parkinson's disease (PD) poses challenges in diagnosis and monitoring due to its progressive nature and complex symptoms. This study introduces a novel approach utilizing surface electromyography (sEMG) to objectively assess PD severity, focusing on the biceps brachii muscle. Initial analysis of sEMG data from five PD patients and five healthy controls revealed significant neuromuscular differences. A traditional Support Vector Machine (SVM) model achieved up to 83% accuracy, while enhancements with a Graph Convolutional Network-Support Vector Machine (GCN-SVM) model increased accuracy to 92%. Despite the preliminary nature of these results, the study outlines a detailed experimental methodology for future research with larger cohorts to validate these findings and integrate the approach into clinical practice. The proposed approach holds promise for advancing PD severity assessment and improving patient care in Parkinson's disease management.

Related papers

• Towards a general-purpose foundation model for fMRI analysis [58.06455456423138]
  We introduce NeuroSTORM, a framework that learns from 4D fMRI volumes and enables efficient knowledge transfer across diverse applications.<n>NeuroSTORM is pre-trained on 28.65 million fMRI frames (>9,000 hours) from over 50,000 subjects across multiple centers and ages 5 to 100.<n>It outperforms existing methods across five tasks: age/gender prediction, phenotype prediction, disease diagnosis, fMRI-to-image retrieval, and task-based fMRI.
  arXiv  Detail & Related papers  (2025-06-11T23:51:01Z)
• Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity [43.108040967674185]
  Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD)<n>This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD.
  arXiv  Detail & Related papers  (2025-02-18T12:01:55Z)
• Advancing Parkinson's Disease Progression Prediction: Comparing Long Short-Term Memory Networks and Kolmogorov-Arnold Networks [1.9022387674252539]
  Parkinson's Disease (PD) is a degenerative neurological disorder that impairs motor and non-motor functions.<n>Current diagnostic methods, however, are often costly, time-consuming, and require specialized equipment and expertise.<n>This work proposes an innovative approach to predicting PD progression using regression methods, Long Short-Term Memory (LSTM) networks, and Kolmogorov Arnold Networks (KAN)
  arXiv  Detail & Related papers  (2024-12-30T06:36:05Z)
• Deep learning for objective estimation of Parkinsonian tremor severity [0.0]
  We introduce a pixel-based deep learning model designed to analyse postural tremor in Parkinson's disease. It was trained on 2,742 assessments from five specialised movement disorder centres across two continents. It detected lateral asymmetry of symptoms, and differentiated between different tremor severities.
  arXiv  Detail & Related papers  (2024-09-03T16:00:34Z)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data [42.96821770394798]
  TACCO is a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data. We conduct experiments on the public MIMIC-III dataset and Emory internal CRADLE dataset over the downstream clinical tasks of phenotype classification and cardiovascular risk prediction. In-depth model analysis, clustering results analysis, and clinical case studies further validate the improved utilities and insightful interpretations delivered by TACCO.
  arXiv  Detail & Related papers  (2024-06-14T14:18:38Z)
• Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A Comprehensive Study [1.2972104025246092]
  This paper introduces an innovative method for diagnosing Parkinson's disease through the analysis of human EEG signals. Our approach incorporates a comprehensive review of EEG signal analysis techniques and machine learning methods. We have engineered an advanced SVM-based model optimized for Parkinson's disease diagnosis.
  arXiv  Detail & Related papers  (2024-04-30T04:25:09Z)
• Survival Prediction Across Diverse Cancer Types Using Neural Networks [40.392772795903795]
  Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies. Medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes. This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
  arXiv  Detail & Related papers  (2024-04-11T21:47:13Z)
• Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms [0.0]
  This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores.
  arXiv  Detail & Related papers  (2023-11-09T15:21:10Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques [0.0]
  Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. EEG indicates abnormalities in PD patients. One major challenge is the lack of a consistent, accurate, and systemic biomarker for PD in order to closely monitor the disease with therapeutic treatments and medication.
  arXiv  Detail & Related papers  (2023-03-29T06:03:05Z)
• Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy [63.20765930558542]
  We have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD) This study provided the largest PD sample size of two-hand synchronous smartwatch measurements.
  arXiv  Detail & Related papers  (2022-05-11T09:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.