EEG spectro-temporal modulation energy: A new feature for automated diagnosis of Alzheimer's disease

There is recent indication that Alzheimer's disease (AD) can be characterized by atypical modulation of electrophysiological brain activity caused by fibrillar amyloid deposition in specific regions of the brain, such as those related to cognition and memory. In this paper, we propose to objectively characterize EEG sub-band modulation in an attempt to develop an automated noninvasive AD diagnostics tool. First, multi-channel full-band EEG signals are decomposed into five well-known frequency sub-bands: delta, theta, alpha, beta, and gamma. The temporal amplitude envelope of each sub-band is then computed via a Hilbert transformation. The proposed `spectro-temporal modulation energy' feature measures the rate with which each sub-band is modulated. Modulation energy features are computed for 19 referential EEG signals and seven bipolar signals. Salient features are then selected and used to train four different classifiers, namely, support vector machines, logistic regression, classification and regression trees, and neural networks. Experiments with a database of 34 participants, 22 of which have been clinically diagnosed with probable-AD, show a neural network classifier achieving over 91% accuracy, thus significantly outperforming a classifier trained with conventional spectral-based features.