Computer-assisted detection of swallowing difficulty. Issue 134 (October 2016)

• Record Type: Journal Article
• Authors: Lee, Jung Chan; Seo, Han Gil; Lee, Woo Hyung; Kim, Hee Chan; Han, Tai Ryoon; Oh, Byung-Mo
• Is Part Of: Computer methods and programs in biomedicine. Issue 134(2016)
• Page Start: 79
• Page End: 88
• Publication Date: 2016-10
• Subjects: Dysphagia -- Swallowing difficulty -- Deglutition disorders -- Hyoid bone -- Support vector machines; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.07.010
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Hyoid movement data attained from videofluoroscopic swallowing study were analyzed. SVM was employed to classify the data as normal or dysfunctional swallowing. Features extracted from hyoid movement were selected to minimize 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. Abstract: To evaluate classification performance of a support vector machine (SVM) classifier for diagnosing swallowing difficulty based on the hyoid movement data attained from videofluoroscopic swallowing study, the hyoid kinematics during the swallowing of 2 mL of liquid barium solution were analyzed for 90 healthy volunteers and 116 dysphagic stroke patients. SVM was used to classify the kinematic results as normal or dysfunctional swallowing. Various kernel functions and kernel parameters were used for optimization. Features were selected to find an optimal feature subset and to minimize redundancy. Accuracy, sensitivity, specificity, and area under a receiving operating characteristic curve (AUC) were used to assess the discrimination performance. In 19 out of 26 features, mean comparison revealed a significant difference between healthy subjects and dysphagic patients. By reducing the number of features to 10, an AUC of 0.9269 could be reached. Common features showing the best classification in both kernel functions included forward maximum excursion time, upward maximum excursion time, maximum excursion length, upward maximum velocity time, upward maximum acceleration time, maximum acceleration, maximum acceleration time, and mean acceleration. SVM-based classification method with the use of kernel functions showed an outstanding (AUC of 0.9269) discrimination performance for either healthy or dysphagic hyoid movement during swallowing. We expect that this classification method will be useful as an adjunct diagnostic tool by providing automatic detection of swallowing dysfunction as well as a research tool providing deeper understanding of pathophysiology.