A novel method of artery stenosis diagnosis using transfer function and support vector machine based on transmission line model: A numerical simulation and validation study. Issue 129 (June 2016)

• Record Type: Journal Article
• Authors: Xiao, Hanguang; Avolio, Alberto; Huang, Decai
• Is Part Of: Computer methods and programs in biomedicine. Issue 129(2016)
• Page Start: 71
• Page End: 81
• Publication Date: 2016-06
• Subjects: Artery stenosis -- Transfer function -- Support vector machine -- Transmission line model -- Stenosis prediction -- Arterial tree; 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.2016.03.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Highlights: A calculation method of transfer function (TF) was proposed by a TLM model of human artery tree. The effects of artery stenosis on the TF were simulated and discussed by a series of simulation. A novel method of artery stenosis diagnosis was proposed and validated by TF and SVM. The accuracies of the method for moderate and serious stenosis were 87% and 99%, respectively. The proposed method is a theoretically feasible method for diagnosis of artery stenosis. Abstract: Background and objective: Transfer function (TF) is an important parameter for the analysis and understanding of hemodynamics when arterial stenosis exists in human arterial tree. Aimed to validate the feasibility of using TF to diagnose arterial stenosis, the forward problem and inverse problem were simulated and discussed. Methods: A calculation method of TF between ascending aorta and any other artery was proposed based on a 55 segment transmission line model (TLM) of human artery tree. The effects of artery stenosis on TF were studied in two aspects: stenosis degree and position. The degree of arterial stenosis was specified to be 10–90% in three representative arteries: carotid, aorta and iliac artery, respectively. In order to validate the feasibility of diagnosis of artery stenosis using TF and support vector machine (SVM), a database of TF was established to simulate the real conditions of artery stenosis based on the TLM model. And a diagnosis model of artery stenosis was built by using SVM and the database. Results: The simulating results showed the modulus and phase of TF were decreasing sharply from frequency 2 to 10 Hz with the stenosis degree increasing and displayed their unique and nonlinear characteristics when frequency is higher than 10 Hz. The diagnosis results showed the average accuracy was above 76% for the stenosis from 10% to 90% degree, and the diagnosis accuracies of moderate (50%) and serious (90%) stenosis were 87% and 99%, respectively. When the stenosis degree increased to 90%, the accuracy of stenosis localization reached up to 94% for most of arteries. Conclusions: The proposed method of combining TF and SVM is a theoretically feasible method for diagnosis of artery stenosis.