Welch Spectral Analysis and Deep Learning Approach for Diagnosing Alzheimer's Disease from Resting-State EEG Recordings

Abstract

Alzheimer's disease (AD) is a serious and progressive neuronal disease that damages brain cells, resulting in loss of cognitive function and memory. Early diagnosis is crucial for medical intervention to prevent brain damage and preserve daily functioning for longer. In this study, a deep learning approach was proposed for early diagnosis of AD from electroencephalography (EEG) recordings at resting-state. The dataset contains EEG recordings of 24 healthy individuals and 24 Alzheimer's patients. To extract the features from the EEG recordings, the power spectral densities of the frequencies between 1-49 Hz were calculated using the welch spectral analysis method. Using extracted features, the performances of random forest (RF), k-nearest neighbor (kNN), support vector machine (SVM), and bidirectional long-short term memory algorithms were compared. In addition, under different resting state conditions (open eyes; closed eyes; open eyes and closed eyes), the effectiveness of EEG signals was analyzed. As a result of the experiments, the bidirectional long-short term memory algorithm had the highest performance. The algorithm achieved promising performance with 98.85% accuracy, 0.986 recall, 0.990 precision, 0.990 specificity, 0.988 f1-score, and 0.977 Matthews correlation coefficient. The combination of the welch spectral analysis and the bidirectional long-short term memory deep learning approach can be used to accurately and effectively distinguish AD and HC groups from resting-state EEG recordings. More accuracy was achieved in this study compared to investigations using cutting-edge technology.

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