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

• URL: http://arxiv.org/abs/2409.05989v1
• Date: Mon, 9 Sep 2024 18:31:39 GMT
• Title: A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data
• Authors: Akshay Sunkara, Sriram Sattiraju, Aakarshan Kumar, Zaryab Kanjiani, Himesh Anumala,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification [11.038002513199299]
  Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia. Recent research has utilized electroencephalography (EEG) data to identify biomarkers that distinguish Alzheimer's Disease patients from healthy individuals.
  arXiv  Detail & Related papers  (2025-04-02T03:29:12Z)
• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images [0.8192907805418583]
  This study delves into the challenging task of classifying Alzheimer's disease into four distinct groups: control normal (CN), progressive mild cognitive impairment (pMCI), stable mild cognitive impairment (sMCI), and Alzheimer's disease (AD) Several deep-learning and traditional machine-learning models have been used to detect Alzheimer's disease. The results show that using deep-learning models to tell the difference between MCI patients gives an overall average accuracy of 93.13% and an AUC of 94.4%.
  arXiv  Detail & Related papers  (2024-03-17T16:12:50Z)
• Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline [54.93591298333767]
  Brain diffuser is a diffusion based end-to-end brain network generative model. It exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects. For the case of Alzheimer's disease, the proposed model performs better than the results from existing toolkits on the Alzheimer's Disease Neuroimaging Initiative database.
  arXiv  Detail & Related papers  (2023-03-11T14:04:58Z)
• Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography [51.45515911920534]
  A task-oriented self-supervised learning approach is proposed to train a more effective anomaly detector. A specific two branch convolutional neural network with larger kernels is designed as the feature extractor. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs.
  arXiv  Detail & Related papers  (2022-07-04T13:15:08Z)
• Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification [48.42347515853289]
  Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD) In this study, we propose an automatic method of determining the duration of TMS induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations.
  arXiv  Detail & Related papers  (2022-04-29T19:27:06Z)
• Predicting Alzheimer's Disease Using 3DMgNet [2.97983501982132]
  3DMgNet is a unified framework of multigrid and convolutional neural network to diagnose Alzheimer's disease (AD) The model achieved 92.133% accuracy for AD vs NC classification and significantly reduced the model parameters.
  arXiv  Detail & Related papers  (2022-01-12T09:08:08Z)
• Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation [0.23931689873603598]
  We propose a deep learning framework to discover the variables which are identifiers of the conversion from MCI to Alzheimer's disease. In particular, the latent space of a variational auto-encoder network trained with the MCI and Alzheimer's patients is manipulated to obtain the significant attributes. By utilizing a generative decoder and the dimensions that lead to the Alzheimer's diagnosis, we generate synthetic dementia patients from MCI patients in the dataset.
  arXiv  Detail & Related papers  (2021-11-16T21:48:09Z)
• Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques [111.165389441988]
  This work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI) Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
  arXiv  Detail & Related papers  (2021-05-18T11:37:57Z)
• A Pilot Study on Visually-Stimulated Cognitive Tasks for EEG-Based Dementia Recognition Using Frequency and Time Features [3.9728427877905568]
  This study aims to investigate the difference in the Electroencephalograph (EEG) signals of three groups of subjects: Normal Control (NC), Mild Cognitive Impairment (MCI), and Dementia (DEM) We have developed a pilot study on machine learning-based dementia diagnosis using EEG signals from four visual stimulation tasks.
  arXiv  Detail & Related papers  (2021-03-05T18:13:23Z)
• Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans [10.120835953459247]
  Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training.
  arXiv  Detail & Related papers  (2020-11-28T14:25:30Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.