An EEG-based approach for Parkinson's disease diagnosis using Capsule network

TL;DR

This paper presents an EEG-based method utilizing capsule networks to diagnose Parkinson's disease with high accuracy, highlighting the significance of gamma band analysis for early detection.

Contribution

It introduces a novel approach that maps EEG frequency band energy to images and applies capsule networks for classification, achieving notable accuracy.

Findings

Achieved 89.34% classification accuracy on EEG data.

Gamma band signals yielded the highest classification accuracy.

Gamma band changes are crucial for early PD detection.

Abstract

As the second most common neurodegenerative disease, Parkinson's disease has caused serious problems worldwide. However, the cause and mechanism of PD are not clear, and no systematic early diagnosis and treatment of PD have been established. Many patients with PD have not been diagnosed or misdiagnosed. In this paper, we proposed an EEG-based approach to diagnosing Parkinson's disease. It mapped the frequency band energy of electroencephalogram(EEG) signals to 2-dimensional images using the interpolation method and identified classification using capsule network(CapsNet) and achieved 89.34% classification accuracy for short-term EEG sections. A comparison of separate classification accuracy across different EEG bands revealed the highest accuracy in the gamma bands, suggesting that we need to pay more attention to the changes in gamma band changes in the early stages of PD.