An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
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 id...
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| Main Authors: | , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/15869/1/Kuryati%20Kipli.pdf http://ir.unimas.my/id/eprint/15869/ http://ieeexplore.ieee.org/document/6609767/ |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| 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 |
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