EEG Based Early Alzheimer Detection Using Artificial Intelligence

Abstract

Alzheimer’s disease is a chronic disease which is characterized by the gradual loss of cognitive functions and the impairment of the ability to perform activities of daily living. This paper presents a new approach for the diagnosis of AD during its early stage through the use of EEG signals and deep learning. EEG is a cheap, noninva sive and portable neuroimaging technique which makes it a potential method for the diagnosis of early stage neurological diseases. The suggested framework incorporates state-of-the-art preprocessing techniques, channel filtering, and Continuous Wavelet Transform (CWT)-based spectrograms for converting the raw EEG signals into time frequency domain. These spectrograms are used as input for the conventional con volutional neural networks (CNNs) including VGG16, ResNet50, InceptionV3 and InceptionResNetV2 for the feature extraction and classification. To determine the models, optimizers, and hyperparameters, several experiments were carried out to find the best fit. It also shows that the ResNet50 model with Adam optimizer and the learning rate of 0.0001 has the best classification performance of 96.69%, which is better than other models. The results also show that the choice of channels, the adjustment of the hyperparameters and the use of deep learning classifiers are crucial to increase the accuracy of the AD detection model based on EEG. This framework offers a convenient, non-invasive and low-cost way for the early diagnosis of AD and thus is applicable for the clinical practice and the field of precision medicine. In the future, the work will be extended in terms of increasing model interpretability and using more data and developing lighter models to be implemented on the edge to in crease the chances of real-life application.