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Wutzl B, Leibnitz K, Rattay F, Kronbichler M, Murata M, Golaszewski SM. Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness. PLoS One 2019; 14:e0219683. [PMID: 31295332 PMCID: PMC6622536 DOI: 10.1371/journal.pone.0219683] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 06/30/2019] [Indexed: 11/18/2022] Open
Abstract
The diagnosis and prognosis of patients with severe chronic disorders of consciousness are still challenging issues and a high rate of misdiagnosis is evident. Hence, new tools are needed for an accurate diagnosis, which will also have an impact on the prognosis. In recent years, functional Magnetic Resonance Imaging (fMRI) has been gaining more and more importance when diagnosing this patient group. Especially resting state scans, i.e., an examination when the patient does not perform any task in particular, seems to be promising for these patient groups. After preprocessing the resting state fMRI data with a standard pipeline, we extracted the correlation matrices of 132 regions of interest. The aim was to find the regions of interest which contributed most to the distinction between the different patient groups and healthy controls. We performed feature selection using a genetic algorithm and a support vector machine. Moreover, we show by using only those regions of interest for classification that are most often selected by our algorithm, we get a much better performance of the classifier.
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Affiliation(s)
- Betty Wutzl
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka University, Osaka, Japan
- Institute for Analysis and Scientific Computing, TU Wien, Vienna, Austria
- Department of Neurology, Paracelsus Medical University, Salzburg, Austria
- * E-mail:
| | - Kenji Leibnitz
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka University, Osaka, Japan
| | - Frank Rattay
- Institute for Analysis and Scientific Computing, TU Wien, Vienna, Austria
| | - Martin Kronbichler
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
- Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Salzburg, Austria
| | - Masayuki Murata
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka University, Osaka, Japan
| | - Stefan Martin Golaszewski
- Department of Neurology, Paracelsus Medical University, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, Vienna, Austria
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