Comparison of Fractal Dimension Algorithms for the Computation of Eeg Biomarkers for Dementia

Analysis of the Fractal Dimension of the EEG appears to be a good approach for the computation of biomarkers for dementia. Several Fractal Dimension algorithms have been used in the EEG analysis of cognitive and sleep disorders. The aim of this paper is to find an accurate Fractal Dimension algorithm that can be applied to the EEG for computing reliable biomarkers, specifically, for the assessment of dementia. To achieve this, some of the common methods for estimating the Fractal Dimension of the EEG are reviewed and compared using serial EEG recordings of normal and subjects with dementia. Biomarkers computed from the Fractal Dimensions are assessed according to their ability to perform early detection, differential diagnosis of dementia and in identifying effects of channel variations in subjects with dementia. The initial findings have shown that not all Fractal Dimension algorithms are suitable for computation of EEG biomarkers for dementia. Typically, biomarkers obtained from the Zero Set and the Adapted Box algorithms have shown good discriminating power in the early detection and differential diagnosis of dementia. Two channels, namely P3 and PZ have also been singled out as the most affected channels in dementing subjects. This bodes well with recent neuroimaging findings which indicate that the posterior cortex is one of the main sites of cortical atrophy in early Alzheimer's Disease.