Continuous Wavelet Transform EEG Features of Alzheimer’s Disease

In this study, we applied the continuous wavelet transform (CWT) to determine electroencephalogram (EEG) discriminating features of Alzheimer’s Disease (AD) patients compared to control subjects. The EEG was recorded from 24 subjects including 10 AD and 14 age-matched control during six sequential resting eyes-closed (EC) and eyes-open (EO) states followed by cognitive tasks and auditory stimulation. We computed the absolute and relative geometric mean powers of Morlet wavelet coefficients at different scale ranges corresponding to the major brain frequency bands. Kruskal-Wallis statistical testing method was then employed to determine the statistically significant features of the cohort geometric means. The results show that there are many discriminating features of AD patients at several different brain major frequency bands, particularly during the second and third EC and EO states. Since many features were identified, a decision tree algorithm was employed to classify the most significant one(s). The algorithm found the absolute power of θ frequency band during the second EO state to be higher for all AD patients when compared to control subjects and identified it as the most significant discriminating feature.