A Robust Parkinson’s Disease Detection Model Based on Time-varying Synaptic Efficacy Function in Spiking Neural Network

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function-based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD. SEFRON explores the advantages of Spiking Neural Network (SNN) which is suitable for neuromorphic devices consuming less energy and higher computational efficiency. To evaluate the performance of SEFRON, a publicly available standard UCI: Oxford Parkinson's Disease Detection Dataset is used. The performance is compared with other well-known neural network models: Multilayer Perceptron Neural Network (MLP-NN) and Radial Basis Function Neural Network (RBF-NN) as well as contemporary SNN models. The experimental results show that SEFRON classifier achieves highest accuracy of 100% and average accuracy of 99.49% which is the highest in comparison to other two classifiers. From the performance, it is proved that the presented model can help to develop a robust, less complex, and energy-efficient automated PD detection device that can assist the physicians to diagnose the disease at its early stage.