A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data
This work addresses the problem of early Alzheimer's diagnosis using EEG data, but it is incremental as it compares existing methods without introducing new ones.
This study compared Artificial Neural Networks (ANNs) and Kolmogorov-Arnold Networks (KANs) for classifying EEG data to detect Alzheimer's Disease, finding that ANNs were more accurate across various parameters.
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.