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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A
Madhav Acharya1, Ravinesh C Deo1, Xiaohui Tao2
1School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia.
Background And Objectives:
Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldwide, posing a significant challenge for its diagnosis and management, leading to detrimental impacts on patients' quality of lives and increased burden on caregivers. Hence, early detection of MCI and AD is crucial for timely intervention and effective disease management.
Methods:
This study presents a comprehensive systematic review focusing on the applications of deep learning in detecting MCI and AD using electroencephalogram (EEG) signals. Through a rigorous literature screening process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research has investigated 74 different papers in detail to analyze the different approaches used to detect MCI and AD neurological disorders.
Results:
The findings of this study stand out as the first to deal with the classification of dual MCI and AD (MCI+AD) using EEG signals. This unique approach has enabled us to highlight the state-of-the-art high-performing models, specifically focusing on deep learning while examining their strengths and limitations in detecting the MCI, AD, and the MCI+AD comorbidity situations.
Conclusion:
The present study has not only identified the current limitations in deep learning area for MCI and AD detection but also proposes specific future directions to address these neurological disorders by implement best practice deep learning approaches. Our main goal is to offer insights as references for future research encouraging the development of deep learning techniques in early detection and diagnosis of MCI and AD neurological disorders. By recommending the most effective deep learning tools, we have also provided a benchmark for future research, with clear implications for the practical use of these techniques in healthcare.
Insights
This review highlights deep learning models for detecting Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) using EEG signals. It identifies limitations and suggests future directions for improved early diagnosis.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Medical Diagnostics
Background:
- Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders impacting millions globally.
- Early detection is critical for effective management and improving patient quality of life.
- Current diagnostic challenges necessitate advanced detection methods.
Purpose of the Study:
- To systematically review deep learning applications for MCI and AD detection using EEG signals.
- To identify high-performing deep learning models and analyze their strengths and limitations.
- To provide insights and benchmarks for future research in early MCI and AD diagnosis.
Main Methods:
- Systematic literature review adhering to PRISMA guidelines.
- Analysis of 74 research papers on deep learning for MCI and AD detection.
- Focus on electroencephalogram (EEG) signal analysis.
Main Results:
- First study to classify dual MCI and AD (MCI+AD) using EEG signals.
- Identified state-of-the-art deep learning models for MCI, AD, and MCI+AD detection.
- Examined the strengths and limitations of various deep learning approaches.
Conclusions:
- Current limitations in deep learning for MCI/AD detection were identified.
- Future research directions and best practice deep learning approaches are proposed.
- Recommendations for effective deep learning tools offer a benchmark for practical healthcare applications.
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