Détail du document
Identifiant

oai:arXiv.org:2409.05989

Sujet
Computer Science - Neural and Evol... Computer Science - Machine Learnin... Electrical Engineering and Systems...
Auteur
Sunkara, Akshay Sattiraju, Sriram Kumar, Aakarshan Kanjiani, Zaryab Anumala, Himesh
Catégorie

Computer Science

Année

2024

Date de référencement

18/09/2024

Mots clés
science disease anns alzheimer
Métrique

Résumé

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.

Sunkara, Akshay,Sattiraju, Sriram,Kumar, Aakarshan,Kanjiani, Zaryab,Anumala, Himesh, 2024, A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data

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