Alzheimer's disease with progression analysis using a novel dilated convolutional attention based long short term
Anusha Rudraraju1, S Venkata Lakshmi2
1Research Scholar, Department of CSE, GITAM School of Technology, GITAM (Deemed to be University), Rushikonda, Visakhapatnam, Andhra Pradesh 530045, India.
Psychiatry Research. Neuroimaging
|May 6, 2026
Summary
A new deep learning framework accurately detects Alzheimer's disease (AD) using EEG signals. This automated system offers high precision for early diagnosis, improving patient quality of life.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Biomedical Engineering
Background:
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder impacting cognition and behavior.
- Early detection of AD is vital for improving patient outcomes and quality of life.
- Current diagnostic methods, including manual neuroimaging analysis, are time-consuming, subjective, and prone to errors.
Purpose of the Study:
- To develop an efficient and automated deep learning framework for early Alzheimer's disease detection and progression prediction.
- To overcome limitations of traditional diagnostic approaches and existing machine learning techniques in handling complex, high-dimensional biomedical data.
Main Methods:
- Utilized the CAU-EEG dataset for Electroencephalography (EEG) signal acquisition.
- Extracted comprehensive features including time, frequency, and time-frequency domain characteristics.
- Implemented a novel deep learning model, dilated convolutions attention based long short term memory (DC-ALSTM), for classification.
Main Results:
- The proposed DC-ALSTM model demonstrated superior performance compared to existing baseline methods.
- Achieved high classification accuracy (99.26%), precision (99.21%), recall (99.23%), and F1-score (99.22%).
- The results indicate outstanding diagnostic capability for Alzheimer's disease.
Conclusions:
- The developed deep learning framework offers a highly accurate and automated solution for Alzheimer's disease diagnosis.
- The DC-ALSTM model shows significant potential for early and reliable detection of AD.
- This approach addresses the need for efficient diagnostic systems in neurodegenerative disease management.
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