Adazd-Net: Automated adaptive and explainable Alzheimer’s disease detection system using EEG signals
View Full Paper
Ask AI
Bookmark
Share
Key result
Adazd-Net achieves ~100% accuracy in detecting Alzheimer's disease from EEG signals.
- n=23
Why the study?
Analyzing rapid, spontaneous EEG signals for Alzheimer's disease is challenging, and clinicians lack trust in existing machine learning models due to poor explainability.
Does the Adazd-Net model improve the accuracy and explainability of Alzheimer's disease detection using EEG signals compared to traditional machine learning models?
Population
23 subjects recruited from the Alzheimer’s Patients’ Relatives Association of Valladolid, Spain.
Comparison
Adazd-Net for automated EEG signal analysis. vs Traditional machine learning models and…
Design
Other
Authors
Discussion
Loading...
Member takes
Implication
May aid early Alzheimer's detection via EEG; leaves open prospective clinical validation before practice adoption.
Structured PICO
Does the Adazd-Net model improve the accuracy and explainability of Alzheimer's disease detection using EEG signals compared to traditional machine learning models?
P
Population
23 older adults (mean age 72.8 years, 48% female), comprising 12 with Alzheimer's disease and 11 with normal cognition, whose EEG signals were retrospectively analyzed to develop a diagnostic model.
I
Intervention
Adazd-Net (adaptive flexible analytic wavelet transform combined with an explainable boosting machine) for automated EEG signal analysis.
C
Comparator
Traditional machine learning models (SVM, KNN, decision tree, ANN, random forest) and traditional flexible analytic wavelet transform (FAWT).
O
Outcome
Accuracy of Alzheimer's disease detection using EEG signals.
The Adazd-Net model provides a highly accurate (99.85%) and explainable method for detecting Alzheimer's disease using EEG signals, potentially aiding clinicians in early diagnosis.
Limitations
- The study used only a single dataset comprising 663 epochs belonging to a small sample size of 23 subjects.
Cite This Study
Khare et al. (2023) studied Alzheimer's disease (n=23). Adazd-Net (Automated adaptive and explainable Alzheimer's disease detection system) vs. Benchmark classifiers (SVM, KNN, DT, ANN, RF) was evaluated on Accuracy of Alzheimer's disease detection (10-fold cross-validation). The Adazd-Net model achieved an accuracy of 99.85%, sensitivity of 99.75%, and specificity of 100% in detecting Alzheimer's disease from EEG signals using ten-fold cross-validation.
View Full Paper
Ask AI
Bookmark
Share