Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals

Background: Alzheimer's disease (AZD) is a degenerative neurological condition that causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early detection and prompt treatment can slow down its progression. AZD can be effectively identified via electroencephalogram (EEG) signals. But, it is challenging to analyze the EEG signals since they change quickly and spontaneously. Additionally, clinicians offer very little trust to the existing models due to lack of explainability in the predictions of machine learning or deep learning models. Method: The paper a novel Adazd-Net which is an adaptive and explanatory framework for automated AZD identification using EEG signals. We propose the adaptive flexible analytic wavelet transform, which automatically adjusts to changes in EEGs. The optimum number of features needed for effective system performance is also explored in this work, along with the discovery of the most discriminant channel. The paper also presents the technique that can be used to explain both the individual and overall predictions provided by the classifier model. Results: We have obtained an accuracy of 99.85% in detecting AZD EEG signals with ten-fold cross-validation strategy. Conclusions: We have suggested a precise and explainable AZD detection technique. Researchers and clinicians can investigate hidden information concerning changes in the brain during AZD using our proposed model. Our developed Adazd-Net model can be employed in hospital scenario to detect AZD, as it is accurate and robust. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).