Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach

arXiv:2404.15335v1 Announce Type: cross
Abstract: Efficient early diagnosis is paramount in addressing the complexities of Parkinson's disease because timely intervention can substantially mitigate symptom progression and improve patient outcomes. In this paper, we present a pioneering deep learning architecture tailored for the binary classification of subjects, utilizing gait cycle datasets to facilitate early detection of Parkinson's disease. Our model harnesses the power of 1D-Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Graph Neural Network (GNN) layers, synergistically capturing temporal …

abstract architecture arxiv cnn complexities cs.lg data deep learning deep learning framework detection diagnosis disease eess.sp framework gnn gru paper parkinson parkinson's parkinson's disease patient sensors type wearable wearable sensors