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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...

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Related Experiment Video

Updated: Jul 12, 2026

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
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An EEG-based systematic explainable detection framework for probing and localizing abnormal patterns in Alzheimer's

Zhenxi Song1, Bin Deng1, Jiang Wang1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China.

Journal of Neural Engineering
|April 22, 2022
PubMed
Summary

This study introduces a deep learning framework for early Alzheimer's disease (AD) detection using electroencephalography (EEG). The method accurately identifies AD patterns by analyzing brain rhythms, complexity, and connectivity, aiding early diagnosis.

Keywords:
Alzheimer’s diseaseelectroencephalographyexplainable learning systemfrequency bandsfunctional networksnonlinear complexity

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Electroencephalography (EEG) offers a low-cost, noninvasive method for early Alzheimer's disease (AD) diagnosis.
  • Identifying AD-specific patterns in EEG is crucial for understanding neurodegeneration and early risk assessment.

Purpose of the Study:

  • To propose a deep learning framework for detecting Alzheimer's disease (AD) abnormalities from short electroencephalography (EEG) sequences.
  • To develop a functional explanatory model for probing AD-related neurodegeneration using EEG data.

Main Methods:

  • A deep learning framework with three encoding pathways analyzing EEG in frequency, complexity, and synchronous domains.
  • Integration of EEG descriptors with neural networks, employing transfer learning and a modified generative adversarial module.
  • Utilization of activation mapping to localize AD-related neurodegeneration across brain rhythms, complexity, and functional connectivity.

Main Results:

  • Achieved 100% accuracy in detecting AD patterns from raw EEG data without extensive preprocessing.
  • Identified abnormal brain rhythm power in frontal lobes, spreading to central lobes for alpha and beta rhythms.
  • Revealed variations in nonlinear complexity across temporal scales and weakened functional connectivity between most brain regions in AD patients.

Conclusions:

  • The proposed framework offers a novel method for revealing EEG abnormalities and their localization in Alzheimer's disease (AD) patients.
  • This study provides a foundation for future work on early identification of individuals at high risk for AD.
  • The method demonstrates superior detection performance compared to existing approaches.