Problem Statement: Alzheimer’s disease (AD) is the most common cause of brain dementia among elderly. Patients with AD are expected to live half as long as those without dementia. Early diagnosis of AD can help extend life quality for probable patients as effective drugs are becoming increasingly available to slow down the progression of the disease. Currently, there is no single clinical test available for accurate diagnosis of AD.
Objectives: The objective of this research is to classify AD and healthy subjects based on EEG analysis of brain cortical connectivity.
Methods: EEGs recorded by 19 scalp electrodes are obtained from 20 AD probable patients and 7 healthy (control) subjects. EEGs are divided into four sub-bands: delta, theta, alpha and beta. Cortical connectivity is evaluated using two methods: pairwise electrode conventional coherence and wavelet coherence. Statistically significant features extracted by the two methods are applied to a probabilistic neural network (PNN) model for classification.
Results: One-way Analysis of Variance (ANOVA) test shows a set of statistically significant differences in electrode coherence between AD and controls. For conventional coherence, AD patients present a significant pattern of increase in the left intrahemispheric frontal coherence in the delta, theta, and alpha bands, an increase in the left intrahemispheric temporo-parietal coherence in all bands, and a decrease in the right temporo-parieto-central coherence in all bands. The decrease in coherence is an indication of lower cortical connectivity. The increase in coherence could be attributed to compensatory mechanisms that attempt to make up for the decrease in memory and cognitive functions caused by the progression of AD. For wavelet coherence, AD coherence values are lower in all cortical regions and within all bands. Features used in the PNN model show that wavelet coherence is a better classifier of AD in single-band analysis, while conventional coherence performs better in mixed-band analysis. The PNN achieves a classification accuracy of 100% using mixed-band features extracted by conventional coherence.
Conclusions: The conventional coherence study presented shows a pattern of decrease in AD coherence, indicating a decline in cortical connectivity. Patterns of increase in AD coherence are attributed to compensatory mechanisms. The wavelet coherence study presents a larger set of statistically significant differences between AD and controls. Features extracted from coherence studies can classify AD from healthy subjects.
Significance: The research shows that coherence studies have potentials in differentiating between healthy elderly and probable AD patients. For the given data set, classification accuracy is 100%.