Dygenn dynamic Gaussian weighted evolving Spiking Neural Network model for enhanced neurological disease detection

Abstract

Spiking Neural Network (SNN) is a promising field for modelling neuronal activity in the brain, with applications in healthcare, agriculture, finance, manufacturing and others. However, traditional SNNs often suffer from static synaptic weights, limiting their adaptability to the changes in data. Hence, to enhance performance and more accurately model the biological process of the brain, a novel SNN model, Dynamic Gaussian-Weighted Evolving Spiking Neural Network (DyGENN) is developed and applied for the detection of Parkinson’s disease and epileptic seizure. DyGENN incorporates a time-varying synaptic weight function modeled by a Gaussian distribution. The output layer evolves dynamically by either adding new output neurons, updating parameters, or skipping sample learning based on the least error margin calculated from postsynaptic firing time and the desired class labels. The suggested model is evaluated on two different imbalanced datasets: the UCI Oxford Parkinson’s Disease Detection and the Bonn Epileptic Seizure dataset using percentage splitting and stratified K-fold cross validation. Compared to popular machine learning (Support Vector Machine (SVM) and Random Forest (RF)), deep learning (Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)) and state-of-the-art SNN frameworks (Norse, SpikingJelly, and a custom PyTorch-based model), DyGENN achieves superior performance: 97.44% accuracy, 87.5% sensitivity, 100% specificity, and geometric mean (G-Mean) of 0.9354 for Parkinson’s disease detection; 97.98% accuracy, 95.83% sensitivity, 98.67% specificity, and G-Mean of 0.972 for epileptic seizure detection. The proposed model outperforms existing models in both scenarios, suggesting its potential for broader application to other neurological conditions characterized by structured biomarker data.