Alzheimer’s disease recognition with artificial neural networks

Alzheimer’s Disease (AD) is the most common cause of dementia, and is well known for its affect on memory loss and other intellectual abilities. The Electroencephalogram (EEG) has been used as a diagnosis tool for dementia for several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and control subjects. For this purpose, two different methodologies and variations were used. The Short Time Fourier Transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands and their associated Spectral Ratios (r1, r2, r3, and r4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8%, and 91.5% of Accuracy.