title: Computer-assisted detection of swallowing difficulty.
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.
authors.affiliation: Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Biomedical Engineering, Seoul National University Hospital, Seoul 03080, Republic of Korea; Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 03080, Republic of Korea.
authors.forename: Jung Chan
authors.initials: JC
authors.lastname: Lee
authors.affiliation: Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
authors.forename: Han Gil
authors.initials: HG
authors.lastname: Seo
authors.affiliation: Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
authors.forename: Woo Hyung
authors.initials: WH
authors.lastname: Lee
authors.affiliation: Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Biomedical Engineering, Seoul National University Hospital, Seoul 03080, Republic of Korea; Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 03080, Republic of Korea.
authors.forename: Hee Chan
authors.initials: HC
authors.lastname: Kim
authors.affiliation: Department of Rehabilitation Medicine, Gangwon-Do Rehabilitation Hospital, Chuncheon 24227, Republic of Korea.
authors.forename: Tai Ryoon
authors.initials: TR
authors.lastname: Han
authors.affiliation: Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea. Electronic address: keepwiz@gmail.com.
authors.forename: Byung-Mo
authors.initials: BM
authors.lastname: Oh
mesh_terms: D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D003680:Deglutition Disorders / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D060388:Support Vector Machine
keywords: Deglutition disorders; Dysphagia; Hyoid bone; Support vector machines; Swallowing difficulty