An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection

Kipli, Kuryati and Kouzani, Abbas Z. 2013, An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection, in EMBC 2013 : Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Piscataway, N.J., pp. 1382-1385.

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Title An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
Author(s) Kipli, Kuryati
Kouzani, Abbas Z.
Conference name IEEE Engineering in Medicine and Biology Society. Conference (35th : 2012 : Osaka, Japan)
Conference location Osaka, Japan
Conference dates 3-7 Jul. 2013
Title of proceedings EMBC 2013 : Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Engineering in Medicine and Biology Society
Start page 1382
End page 1385
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Brain volume changes at structural level appear to have utmost importance in depression biomarkers studies. However, these brain volumetric findings have very minimal utilization in depression detection studies at individual level. Thus, this paper presents an evaluation of volumetric features to identify the relevant/optimal features for the detection of depression. An algorithm is presented for determination of rank and degree of contribution (DoC) of structural magnetic resonance imaging (sMRI) volumetric features. The algorithm is based on the frequencies of each feature contribution toward the desired accuracy limit. Forty-four volumetric features from various brain regions were adopted for evaluation. From DoC analysis, the DoC of each volumetric feature for depression detection is calculated and the features that dominate the contribution are determined.
ISBN 1457702142
9781457702143
Language eng
Field of Research 090609 Signal Processing
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057143

Document type: Conference Paper
Collection: School of Engineering
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