TY - GEN
T1 - A Mahalanobis distance based approach towards the reliable detection of geriatric depression symptoms co-existing with cognitive decline
AU - Frantzidis, Christos A.
AU - Diamantoudi, Maria D.
AU - Grigoriadou, Eirini
AU - Semertzidou, Anastasia
AU - Billis, Antonis
AU - Konstantinidis, Evdokimos
AU - Klados, Manousos A.
AU - Vivas, Ana B.
AU - Bratsas, Charalampos
AU - Tsolaki, Magda
AU - Pappas, Constantinos
AU - Bamidis, Panagiotis D.
PY - 2012
Y1 - 2012
N2 - Geriatric depression is a highly frequent medical condition that influences independent living and social life of senior citizens. It also affects their medical condition due to reduced commitment to the appropriate treatment. Coexistence of depressive symptoms in Mild Cognitive Impairment (MCI) and lack of objective tools towards their reliable distinction from neurodegeneration, motivated this study to propose a computerized approach of depression recognition. Resting state electroencephalographic data of both rhythmic activity and synchronization features were extracted and the Mahalanobis Distance (MD) classifier was adopted in order to differentiate 33 depressive patients from an equal number of age-matched controls. Both groups demonstrated cognitive decline within the context of MCI. The promising results (89.39% overall classification accuracy, 93.94% sensitivity and 84.85% specificity) imply that combination of neurophysiological (EEG) and neuropsychological tools with pattern recognition techniques may provide an integrative diagnosis of geriatric depression with high accuracy.
AB - Geriatric depression is a highly frequent medical condition that influences independent living and social life of senior citizens. It also affects their medical condition due to reduced commitment to the appropriate treatment. Coexistence of depressive symptoms in Mild Cognitive Impairment (MCI) and lack of objective tools towards their reliable distinction from neurodegeneration, motivated this study to propose a computerized approach of depression recognition. Resting state electroencephalographic data of both rhythmic activity and synchronization features were extracted and the Mahalanobis Distance (MD) classifier was adopted in order to differentiate 33 depressive patients from an equal number of age-matched controls. Both groups demonstrated cognitive decline within the context of MCI. The promising results (89.39% overall classification accuracy, 93.94% sensitivity and 84.85% specificity) imply that combination of neurophysiological (EEG) and neuropsychological tools with pattern recognition techniques may provide an integrative diagnosis of geriatric depression with high accuracy.
KW - Electroencephalography
KW - Geriatric Depression
KW - Mahalanobis Distance Classifier
KW - Mild Cognitive Impairment
KW - Neuropsychological Estimation
KW - Wavelet Entropy
UR - http://www.scopus.com/inward/record.url?scp=84870900670&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007%2F978-3-642-33412-2_2
U2 - 10.1007/978-3-642-33412-2_2
DO - 10.1007/978-3-642-33412-2_2
M3 - Conference publication
AN - SCOPUS:84870900670
SN - 9783642334115
VL - 382 AICT
T3 - IFIP Advances in Information and Communication Technology
SP - 16
EP - 25
BT - Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops
T2 - 8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB
Y2 - 27 September 2012 through 30 September 2012
ER -