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  Detecting neurophysiological alterations during Mild Cognitive Impairment and Dementia using wavelet-based energy computation and a Mahalanobis Distance classifier

Frantzidis, C., Semertzidou, A., Ladas, A., Karagianni, M., Lithari, C., Kyrillidou, A., et al. (2011). Detecting neurophysiological alterations during Mild Cognitive Impairment and Dementia using wavelet-based energy computation and a Mahalanobis Distance classifier. Neuroscience Letters, 500(Suppl.), e53-e53. doi:10.1016/j.neulet.2011.05.225.

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 Creators:
Frantzidis, Christos1, Author
Semertzidou, Anastasia1, Author
Ladas, Aristea2, Author
Karagianni, Maria1, Author
Lithari, Chrysa1, Author
Kyrillidou, Athina1, Author
Grigoriadou, Eirini1, Author
Klados, Manousos1, Author              
Vivas, Ana3, Author
Kounti, Fotini4, Author
Tsolaki, Magda5, Author
Pappas, Costas1, Author
Bamidis, Panagiotis1, Author
Affiliations:
1Laboratory of Medical Informatics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece, ou_persistent22              
2Psychology Department, The University of Sheffield International Faculty, Thessaloniki, Greece, ou_persistent22              
3City Liberal Studies, Thessaloniki, Greece, ou_persistent22              
4Department of Psychology, Aristotle University of Thessaloniki, Greece, ou_persistent22              
53rd Department of Neurology, Medical School, Aristotle University of Thessaloniki, Greece, ou_persistent22              

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 Abstract: Recently, a transitional stage, called Mild Cognitive Impairment (MCI) has been identified. Early MCI detection is of crucial importance for preventing dementia onset. The aim of this study is to provide a classification framework able to discriminate subtle alterations due to neurodegenerative processes. Primary attention was given at the MCI stage. Therefore two MCI groups were formed according the patient's performance in the Montreal Cognitive Assessment (MoCA) test; a group of 39 patient with a low cognitive decline (MCI-1; MoCA ≥ 25), and a group of 31 patients with moderate cognitive decline group (MCI-2; MoCA < 25). In addition, we tested 17 healthy control participants, and 14 mild demented patients. Participants underwent a full neuropsychologic examination. Application of the Independent Component Analysis (ICA) and visual inspection of EEG data during resting state condition with eyes closed was initially adopted for noise rejection. Then, the energy for each frequency band was computed through discrete wavelet transform (DWT). These spectral components for 57 electrodes served as an input to a classification system employing Mahalanobis Distance. Classification results (84.16% overall accuracy) demonstrated the system's robustness and reliability. Discrimination reached 82.35% for healthy controls, 92.31% for MCI-1, 74.19% for MCI-2 and 85.71% for mild demented patients. The classification system is proposed in order to supplement the neuropsychologic examination and to correlate subtle cognitive deficits revealed by MoCA with modified neurophysiological patterns, providing thus a better understanding to the progression of neurodegenerative mechanisms.

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Language(s): eng - English
 Dates: 2011-07-112011-07
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neulet.2011.05.225
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Title: SAN Meeting 2011
Place of Event: Thessaloniki, Greece
Start-/End Date: 2011-05-05 - 2011-05-08

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Title: Neuroscience Letters
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 500 (Suppl.) Sequence Number: - Start / End Page: e53 - e53 Identifier: ISSN: 0304-3940
CoNE: https://pure.mpg.de/cone/journals/resource/954925512448