David M. Devilbiss

Discrete wavelet transform EEG features of Alzheimer'S disease in activated states

In this study, electroencephalogram (EEG) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimers disease (AD) and 14 age-matched Controls (CN) - were analyzed using Discrete Wavelet Transform (DWT). The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory tasks, since AD is characterized by progressive impairments in cognition and memory. At each recording block, DWT extracts EEG features corresponding to major brain frequency bands. T-test and Kruskal-Wallis methods were used to determine the statistically significant features of EEG signals from AD patients compared to Controls. A decision tree algorithm was then used to identify the dominant features for AD patients. It was determined that the mean value of the low-δ (1 - 2 Hz) frequency band during the Paced Auditory Serial Addition Test with 2.0 (s) interval and the mean value of the δ frequency band (12 - 30 Hz) during 6 Hz auditory stimulation have higher mean values in AD patients than Controls. Due to artifacts, the less reliable low-δ features were removed and it was determined that the mean value of β frequency band during 6 Hz auditory stimulation followed by the standard deviation of θ (4 - 8 Hz) frequency band of one card learning cognitive task are higher for AD patients compared to Controls and thus the most dominant discriminating features of the disease.

Power based analysis of single-electrode human EEG recordings using continuous wavelet transform

The purpose of this paper is to demonstrate the capabilities of continuous wavelet transform (CWT) in analyzing electroencephalogram (EEG) signals produced through a single-electrode recording device. Further, CWT is used to evaluate standard fast Fourier transform (FFT) analysis results. Sequential resting eyes-closed (EC) and eyes-open (EO) EEG signals, recorded from individuals during a one year period (N = 25), are analyzed. The absolute and relative geometric mean powers of the EEG δ, θ, α, and β-bands are calculated using FFT and CWT analysis. A sliding Blackman window based FFT analysis shows a statistically significant α and β-band dominant peaks for EC compared to EO recordings. These results confirm well-known results reported in the literature, which validates the EEG recording device. CWT analysis using Morlet mother function results are consistent with those of FFT analysis and revealed additional differences where a second range of statistically significant dominant scales are clearly observed in the δ-band for EO compared with EC, which has not been reported in the literature. However, the difference between EO and EC power spectra in the β range is less significant in the wavelet analysis.

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.Copyright

A noninvasive device-based approach to aid in the diagnosis of mTBI and Alzheimer's disease: Preliminary findings from Clinical Pilot Studies

and might be due to increased vulnerability of their memory networks. Conclusions: The higher absolute power of upper alpha in resting EEG and the increased upper alpha desynchronization during cognitive task in non-demented older individuals carrying AD risk variant CLU CC implies unfavorable effect of this genotype on brain cholinergic pathways and hippocampus long before the presumable dementia. The results suggest that the neurophysiological markers may be important in monitoring disease progression in at-risk elderly.