Bi-cephalic self-attended model to classify Parkinson's disease patients with freezing of gait

• URL: http://arxiv.org/abs/2507.20862v1
• Date: Mon, 28 Jul 2025 14:16:01 GMT
• Title: Bi-cephalic self-attended model to classify Parkinson's disease patients with freezing of gait
• Authors: Shomoita Jahid Mitin, Rodrigue Rizk, Maximilian Scherer, Thomas Koeglsperger, Daniel Lench, KC Santosh, Arun Singh,
• Abstract summary: This study aims to develop an objective, data-driven, and multi-modal classification model to detect gait dysfunction in PD patients.<n>Features extracted from resting-state EEG and descriptive variables were used to train a novel Bi-cephalic Self-Attention Model (BiSAM)
• Score: 2.8658669653771334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parkinson Disease (PD) often results in motor and cognitive impairments, including gait dysfunction, particularly in patients with freezing of gait (FOG). Current detection methods are either subjective or reliant on specialized gait analysis tools. This study aims to develop an objective, data-driven, and multi-modal classification model to detect gait dysfunction in PD patients using resting-state EEG signals combined with demographic and clinical variables. We utilized a dataset of 124 participants: 42 PD patients with FOG (PDFOG+), 41 without FOG (PDFOG-), and 41 age-matched healthy controls. Features extracted from resting-state EEG and descriptive variables (age, education, disease duration) were used to train a novel Bi-cephalic Self-Attention Model (BiSAM). We tested three modalities: signal-only, descriptive-only, and multi-modal, across different EEG channel subsets (BiSAM-63, -16, -8, and -4). Signal-only and descriptive-only models showed limited performance, achieving a maximum accuracy of 55% and 68%, respectively. In contrast, the multi-modal models significantly outperformed both, with BiSAM-8 and BiSAM-4 achieving the highest classification accuracy of 88%. These results demonstrate the value of integrating EEG with objective descriptive features for robust PDFOG+ detection. This study introduces a multi-modal, attention-based architecture that objectively classifies PDFOG+ using minimal EEG channels and descriptive variables. This approach offers a scalable and efficient alternative to traditional assessments, with potential applications in routine clinical monitoring and early diagnosis of PD-related gait dysfunction.

Related papers

• 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)
• Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction [45.89562183034469]
  Existing deep learning diagnosis prediction models with intrinsic interpretability often assign attention weights to every past diagnosis or hospital visit.<n>We introduce SHy, a self-explaining hypergraph neural network model, designed to offer personalized, concise and faithful explanations.<n> SHy captures higher-order disease interactions and extracts distinct temporal phenotypes as personalized explanations.
  arXiv  Detail & Related papers  (2025-02-15T06:33:02Z)
• Machine Learning-Based Differential Diagnosis of Parkinson's Disease Using Kinematic Feature Extraction and Selection [0.6249768559720121]
  Parkinson's disease (PD) is the second most common neurodegenerative disorder.<n>PD shares symptoms with other neurodegenerative diseases, such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA)<n>We propose a machine learning-based system for differential diagnosis of PD, PSP, MSA, and healthy controls.
  arXiv  Detail & Related papers  (2025-01-02T14:43:39Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• MADE-for-ASD: A Multi-Atlas Deep Ensemble Network for Diagnosing Autism Spectrum Disorder [4.7377709803078325]
  This paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance.
  arXiv  Detail & Related papers  (2024-07-09T17:49:23Z)
• Methodology and Real-World Applications of Dynamic Uncertain Causality Graph for Clinical Diagnosis with Explainability and Invariance [41.373856519548404]
  Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios. 46 DUCG models covering 54 chief complaints were constructed. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified.
  arXiv  Detail & Related papers  (2024-06-09T11:37:45Z)
• Evaluating Echo State Network for Parkinson's Disease Prediction using Voice Features [1.2289361708127877]
  This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives. Various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated. ESN consistently maintains a false negative rate of less than 8% in 83% of cases.
  arXiv  Detail & Related papers  (2024-01-28T14:39:43Z)
• A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals [8.526741765074677]
  This study presents a deep learning-based model for the diagnosis of Parkinson's disease (PD) using resting state electroencephalogram (EEG) signal. The model is designed using a hybrid model, consists of convolutional neural network (CNN), bidirectional gated recurrent unit (Bi-GRU) and attention mechanism. The results show that the proposed model can accurately diagnose PD with high performance on both the training and hold-out datasets.
  arXiv  Detail & Related papers  (2023-08-14T20:06:19Z)
• A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics [63.106382317917344]
  We report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases.
  arXiv  Detail & Related papers  (2023-06-01T16:23:47Z)
• Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis [0.0]
  We propose a new approach inspired by the functioning of the brain that uses the dynamics, frequency and temporal content of EEGs to extract new demarcating features of the disease. The method was evaluated on a publicly available dataset containing EEG signals recorded during a 3-oddball auditory task involving N = 50 subjects, of whom 25 suffer from PD.
  arXiv  Detail & Related papers  (2022-10-20T22:39:29Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

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.