Prospects for routine detection of dementia using the fractal dimension of the human electroencephalogram

The paper details research which aims to improve the contribution made by electroencephalogram (EEG) analysis to the diagnosis and care of patients with brain disease; dementia in particular. Previous attempts to automate EEG analysis have concentrated on separating patient groups from control groups, often on the basis of a single neurophysiological index derived from a short, isolated segment of EEG. The authors seek to develop, and test, a novel technique for the analysis of changes in serial EEG recordings on individuals (subject-specific analysis) which may serve as a basis for routine early detection of dementia. The objectives of the reported study were to examine the feasibility of applying appropriate fractal dimension (FD) (complexity) measures to the human EEG, and to examine whether methods using the subject specific variability of these measures are likely to be useful for detecting patients who develop dementia. The reason for undertaking the study was to establish a `proof of concept´ and determine whether research should concentrate in this area. Existing EEG analysis methods were reviewed and four FD measures suitable for EEG analysis were developed. These four measures were applied to a total of 21 EEG recordings (from seven subjects with various dementias, eight age matched controls and two young subjects who gave three recordings each), The results were analysed and the following conclusions were drawn: it is possible to measure the complexity of the human EEG using the FD, and the subject specific variability of the FD is an important candidate method for identifying patients with dementia. Therefore, further work in this area is justified