Abstract
Abstract
Alzheimer's disease (AD) is characterized by deficits in cognition, behavior, and intellectual functioning, and Mild Cognitive Impairment (MCI) refers to individuals whose cognitive impairment deviates from what is expected for their age but does not significantly interfere with daily activities. Because there is no treatment for AD, early prediction of AD can be helpful to reducing the progression of this disease. This study examines the Electroencephalography (EEG) signal of 3 distinct groups including AD, MCI, and healthy individuals. Recognizing the non-stationary nature of EEG signals, two nonlinear approaches, Poincare and Entropy, are employed for meaningful feature extraction. To extract features from EEG signal, data should segmented into epochs and for each one, feature extraction approaches are implemented. The obtained features are given to machine learning algorithms to classify the subjects. Extensive experiments were conducted to analyze the features comprehensively The results demonstrate that, our proposed method surpasses previous studies in terms of accuracy, sensitivity, and specificity, indicating its effectiveness in classifying individuals with AD, MCI, and those without cognitive impairment.
Publisher
Research Square Platform LLC
Reference50 articles.
1. The global prevalence of dementia: a systematic review and metaanalysis;Prince M;Alzheimer's & dementia,2013
2. Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier;Feng J;Artificial Intelligence in Medicine,2020
3. Nguyen, M., He, T., An, L., Alexander, D. C., Feng, J., Yeo, B. T., & Alzheimer's Disease Neuroimaging Initiative. (2020). Predicting Alzheimer's disease progression using deep recurrent neural networks. NeuroImage, 222, 117203.
4. Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning;Lee E;NeuroImage,2019
5. Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models;Liang SF;IEEE Transactions on Instrumentation and Measurement,2012