Park-Net: Multi-modality qEEG and fMRI-based PDDiagnosis via Attentional Graph Convolutional Neural Network

 

S. Mohanapriya1,*, Kamalraj Subramaniam2

1Research scholar, Department of Computer Science and Engineering, Karpagem Academy of Higher Education, India

2Professor and Head, Department of Biomedical Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, India

Text Box: Abstract Parkinson's disease (PD) is a degenerative neurological condition instigated by the death of dopamine-producing neurons in the brain, which is manifested as tremors, rigidity, bradykinesia, and postural instability. Early and accurate diagnosis of PD is crucial for timely initiation of appropriate treatment strategies, which can help alleviate symptoms, advance excellence of life, and hypothetically leisurely disease development. A promising method for PD diagnosis is the combination of fMRI and qEEG methods, which provide full neuroimaging data to improve accuracy and early detection. However, recent studies are limited in performing and achieving accurate PD diagnosis. To alleviate this issue, we have proposed graph neural network-based PD diagnosis model addressed as Park-Net. Here, data pre-treatment is initially implemented in which both collected qEEG signal and fMRI image is denoised using Discrete Wavelet Transform (DWT) and Improved Kalman Filter (IKF) respectively. Following that, appropriate region of fMRI is segmented by adversarial network-based U-Net (AN-Net). After that, segmented region is fed into proposed Park-Net model; here modality encoder (ME) encompassed Long Short-Term Memory (LSTM) for feature extraction. We adapted Multi-modal Fused Attentional Graph Convolutional Neural Network (MAGCN) for constructing graph based on feature correlation and then fused. Finally, we designed Self-Attention Pooling with softmax layer for classifying PD as normal or abnormal. We have implemented our proposed Park-Net model to evaluate model performance, and its efficacy is assessed using a range of performance metrics such as accuracy, sensitivity, specificity, F1-Score, and ROC curve, highlighting its superior performance compared to existing methods in PD diagnosis approaches. Emails: smohanapriya3@gmail.com; kamalrajee@gmail.com

Received: January 29, 2025 Revised: March 01, 2025 Accepted: April 07, 2025

 

Keywords: Multi-modal; Parkinson’s disease; qEEG and fMRI; Attentional Graph Convolutional Neural Network; Self-Attention