A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data

TL;DR

This study compares the effectiveness of Artificial Neural Networks and Kolmogorov-Arnold Networks in classifying EEG data for Alzheimer's diagnosis, finding ANNs more accurate across various parameters.

Contribution

It provides a comprehensive comparison between ANNs and KANs for EEG-based Alzheimer's classification, highlighting the superior accuracy of ANNs.

Findings

ANNs outperform KANs in accuracy across multiple parameters

EEG signals show distinct differences between Alzheimer's and non-Alzheimer's patients

ANNs have lower false positive rates in diagnosis

Abstract

Alzheimer's Disease is an incurable cognitive condition that affects thousands of people globally. While some diagnostic methods exist for Alzheimer's Disease, many of these methods cannot detect Alzheimer's in its earlier stages. Recently, researchers have explored the use of Electroencephalogram (EEG) technology for diagnosing Alzheimer's. EEG is a noninvasive method of recording the brain's electrical signals, and EEG data has shown distinct differences between patients with and without Alzheimer's. In the past, Artificial Neural Networks (ANNs) have been used to predict Alzheimer's from EEG data, but these models sometimes produce false positive diagnoses. This study aims to compare losses between ANNs and Kolmogorov-Arnold Networks (KANs) across multiple types of epochs, learning rates, and nodes. The results show that across these different parameters, ANNs are more accurate in predicting Alzheimer's Disease from EEG signals.