Computer-assisted detection of swallowing difficulty
Highlights • Hyoid movement data attained from videofluoroscopic swallowing study was analyzed. • SVM was employed to classify the data as normal or dysfunctional swallowing. • Features extracted from hyoid movement were selected to minimized redundancy. • Feature selection results would present a d...
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| Veröffentlicht in: | Computer methods and programs in biomedicine 2016-10, Vol.134, p.79-88 |
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| container_title | Computer methods and programs in biomedicine |
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| creator | Lee, Jung Chan Seo, Han Gil Kim, Hee Chan Han, Tai Ryoon Oh, Byung-Mo |
| description | Highlights • Hyoid movement data attained from videofluoroscopic swallowing study was analyzed. • SVM was employed to classify the data as normal or dysfunctional swallowing. • Features extracted from hyoid movement were selected to minimized redundancy. • Feature selection results would present a deeper understanding of dysphagia pathophysiology. • The proposed method with an outstanding discrimination performance would be useful as an adjunct diagnostic tool. |
| doi_str_mv | 10.1016/j.cmpb.2016.07.010 |
| format | Article |
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| ispartof | Computer methods and programs in biomedicine, 2016-10, Vol.134, p.79-88 |
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| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Biomechanical Phenomena Case-Control Studies Deglutition disorders Deglutition Disorders - diagnosis Diagnosis, Computer-Assisted Dysphagia Humans Hyoid bone Internal Medicine Other Support Vector Machine Support vector machines Swallowing difficulty |
| title | Computer-assisted detection of swallowing difficulty |
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