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Advisor(s)
Abstract(s)
Alzheimer’s disease (AD) is a neurocognitive illness that leads to dementia and mainly affects the elderly. As the percentage of
old people is strongly increasing worldwide, it is urgent to develop contributions to solve this complex problem. The early diagnosis
at AD first stage known as Mild Cognitive Impairment (MCI) needs a better accuracy and there is not a biomarker able to detect
AD without invasive tests. In this study, Electroencephalogram (EEG) signals have been used to serve as a way of finding
parameters to improve AD diagnosis in first stages. For that, a hybrid method based on a Cepstral analysis of EEG Discrete Wavelet
Transform (DWT) multiband decomposition was developed. Several Cepstral Distances (CD) were extracted to verify the lag
between cepstra of conventional bands signals. The results showed that this hybrid method is a good tool for describing and
distinguishing the AD EEG activity along its different stages because several statistically significant parameters variations were
found between controls, MCI, moderate AD and advanced AD (the lowest p-value=0.003<0.05).
Description
Keywords
Alzheimer’s diasease Early diagnosis Cepstral analisys Wavelet transform Electroencephalogram signals Cepstral distances
Pedagogical Context
Citation
Rodrigues, P.M., Freitas, D., Teixeira, J.P., Bispo, B., Alves, D., Garrett, C. (2018). Electroencephalogram hybrid method for alzheimer early detection. In CENTERIS - International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2018, Lisboa, Portugal, 21-23 November 2018. Procedia Computer Science, 138, 209–214
Publisher
Elsevier