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dc.contributor.authorGoh, Cindy-
dc.contributor.authorIfeachor, C.-
dc.contributor.authorCamilleri, Tracey A.-
dc.contributor.authorLatchoumane, Charles-Francois Vincent-
dc.contributor.authorBigan, Cristin-
dc.contributor.authorHenderson, G.-
dc.contributor.authorCamilleri, Kenneth P.-
dc.contributor.authorFabri, Simon G.-
dc.contributor.authorJeong, J.-
dc.contributor.authorHudson, N.-
dc.contributor.authorCapotosto, P.-
dc.contributor.authorWimalaratna, S.-
dc.contributor.authorBesleaga, Mircea-
dc.identifier.citationGoh, C., Ifeachor, E., Cassar, T., Latchoumane, C. F. V., Bigan, C., Henderson, G.,...Besleaga, M. (2007). Comparison of methods for early detection of Alzheimer's disease. Third International Conference on Computational Intelligence in Medicine and Healthcare CIMED07, Plymouth.en_GB
dc.description.abstractIn this paper, six methods for early detection of AD - fractal dimension (FD), source localization (SL), Hjorth analysis, cross mutual information (CMI), pdf of zero-crossing intervals (ZCI) and power spectrum (PS) are compared. We selected these methods because they were relatively insensitive to artefacts and gave promising results. The methods were applied to the EEGs of 38 mild AD patients and 45 normal subjects, to extract markers for early detection of AD. The datasets were obtained from three different countries and hence provided a more rigorous comparison. The performances of each method were measured using ROC analysis, sensitivity, specificity, classification accuracy, z-score and Area under ROC curve. Results showed that indices found using time domain methods, such as FD and ZCI outperform those obtained from frequency analysis. In particular, ZCI had the best overall performance for at least 50% of the datasets. We found that, although the PS results tend to agree with SL and ZCI, it is more sensitive to shifts in the alpha-theta rhythms. Results also show that FD and ZCI could potentially be use for early detection as their performances outperformed those used in current clinical AD diagnosis (sensitivity > 55%, specificity > 83%).en_GB
dc.subjectAlzheimer's diseaseen_GB
dc.subjectAlzheimer's disease -- Age factorsen_GB
dc.subjectAlzheimer's disease -- Diagnosisen_GB
dc.subjectDementia -- Diagnosisen_GB
dc.subjectElectroencephalography -- Data processingen_GB
dc.titleComparison of methods for early detection of Alzheimer's diseaseen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.CIMEDen_GB
dc.bibliographicCitation.conferencenameThird International Conference on Computational Intelligence in Medicine and Healthcare CIMED07en_GB
dc.bibliographicCitation.conferenceplacePlymouth, UK, 2007en_GB
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