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Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ and tau...
Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
Dementia l: Introduction01:22

Dementia l: Introduction

Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...
Dementia01:30

Dementia

Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual.

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Related Experiment Video

Updated: May 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Improving Alzheimer's disease diagnosis with machine learning techniques.

Lucas R Trambaiolli1, Ana C Lorena, Francisco J Fraga

  • 1Mathematics, Computing and Cognition Center (CMCC), Universidade Federal do ABC (UFABC), São Paulo, Brazil.

Clinical EEG and Neuroscience
|August 30, 2011
PubMed
Summary

Machine learning accurately differentiates Alzheimer's disease (AD) patients from controls using electroencephalography (EEG) patterns. This quantitative EEG (qEEG) method shows promise for earlier AD diagnosis and treatment monitoring.

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Last Updated: May 29, 2026

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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) diagnosis currently lacks a specific test, relying on a combination of clinical history, neuropsychological assessments, laboratory tests, neuroimaging, and electroencephalography (EEG).
  • There is a critical need for novel diagnostic approaches to enable earlier detection of AD and to effectively monitor treatment outcomes.
  • Quantitative EEG (qEEG) analysis presents a potential avenue for developing more objective and sensitive diagnostic tools.

Purpose of the Study:

  • To investigate the efficacy of a Machine Learning (ML) technique, specifically Support Vector Machine (SVM), in identifying patterns within EEG data to distinguish between individuals with Alzheimer's disease and healthy controls.
  • To develop and validate a qEEG processing method capable of automatically differentiating AD patients from normal individuals.
  • To assess the potential of this ML-based qEEG approach as a complementary tool for the diagnosis of probable dementia.

Main Methods:

  • Utilized Support Vector Machine (SVM), a Machine Learning algorithm, to analyze electroencephalography (EEG) epochs.
  • Processed quantitative EEG (qEEG) data from a cohort of 19 healthy subjects and 16 patients diagnosed with mild to moderate Alzheimer's disease.
  • Developed an automated method for pattern recognition in EEG signals to differentiate between the two groups.

Main Results:

  • The developed qEEG processing method achieved an accuracy of 79.9% and a sensitivity of 83.2% in differentiating AD patients from controls based on EEG epochs.
  • When considering the diagnosis of each individual patient, the analysis demonstrated higher performance metrics, reaching 87.0% accuracy and 91.7% sensitivity.
  • These findings indicate a significant capability of the ML-based qEEG approach in distinguishing between Alzheimer's disease and normal cognitive states.

Conclusions:

  • The study successfully developed and validated a Machine Learning-based quantitative EEG (qEEG) method for the automatic differentiation of Alzheimer's disease patients from healthy individuals.
  • This qEEG approach shows considerable potential as a non-invasive, complementary tool to aid in the earlier and more accurate diagnosis of probable dementia.
  • Further research and validation in larger, diverse cohorts are warranted to establish this method in clinical practice for Alzheimer's disease diagnosis and management.