This paper makes an outline case for the need for a low-cost, easy to administer method for detecting dementia within the growing at risk population. It proposes two methods for electroencephalogram (EEG) analysis for detecting dementia that could fulfil such a need. The paper describes a fractal dimension-based method for analyzing the EEG waveforms of subjects with dementia and reports on an assessment which demonstrates that an appropriate fractal dimension measure could achieve 67% sensitivity to probable Alzheimer's disease (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. An alternative method based on the probability density function of the zero-crossing intervals is shown to achieve 78% sensitivity to probable Alzheimer's disease and an estimated sensitivity to probable Vascular (or mixed) dementia of 35% (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. This compares well with other studies, reported by the American Academy of Neurology, which typically provide a sensitivity of 81% and specificity of 70%. The EEG recordings used to assess these methods included artefacts and had no a priori selection of elements "suitable for analysis." This approach gives a good prediction of the usefulness of the methods, as they would be used in practice. A total of 39 patients (30 probable Alzheimer's Disease, six Vascular Dementia and three mixed dementia) and 42 healthy volunteers were involved in the study. However, although results from the preliminary evaluation of the methods are promising, there is a need for a more extensive study to validate the methods using EEGs from a larger and more varied patient cohorts with neuroimaging results, to exclude other causes and cognitive scores to correlate results with severity of cognitive status.

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An important trend in medical technology is towards support for personalised healthcare, fuelled by developments in genomic-based medicine. New computational intelligent techniques for biodata analysis will be needed to fully exploit the vast amounts of data that are being generated. Non-linear signal processing methods will form an important part of such computational intelligent techniques. This paper introduces some non-linear methods which are likely to play a role in the emerging area of biopattern and bioprofile analysis that will underpin personalized healthcare. We highlight their application to clinical problems involving EEG and fetal ECG and heart rate analysis, and issues that arise when they are applied to real world problems. The clinical problems include dementia assessment, drug administration and fetal monitoring. The potential role and challenges in the application of non-linear signal analysis of biopattern and bioprofile are highlighted within the context of a major EU project, BIOPATTERN.

Worldwide, the number of people that develop Alzheimer's disease and other types of dementia is rapidly rising and will create a considerable financial burden on the health and social services. The availability of new drugs that may slow or even halt the disease progression makes accurate early detection crucial. Objective methods are needed to support clinical diagnosis and care for patients; to quantify severity, monitor progression and response to new treatments. Electrophysiological markers have an important role to play in the objective assessment and care for dementia. The EEG provides a measure of brain dysfunction and EEG changes could be detected fairly early in the dementing process. Subject-specific EEG analysis offers the possibility of using objective methods to assess and care for dementia on an individual basis. The main objectives of this paper are: (i) to introduce the concepts of subject-specific EEG analysis as a basis for improving diagnosis and care for dementia; and (ii) present two novel methods for deriving suitable subject-specific electrophysiological markers analysis of fractal dimension and zero crossing interval density of the EEG. We present findings that indicate that the methods are potentially good candidates for the development of individualized, low-cost, easy to administer and reasonably accurate methods for detecting dementia within the growing at risk population.