title: 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.
abstract: 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 10Hz with the stenosis degree increasing and displayed their unique and nonlinear characteristics when frequency is higher than 10Hz. 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.
authors.affiliation: Chongqing Key Laboratory of Modern Photoelectric Detection Technology and Instrument, School of Optoelectronic Information, Chongqing University of Technology, No. 69 Hongguang Road, Banan District, Chongqing 400050, PR China. Electronic address: simenxiao1211@163.com.
authors.forename: Hanguang
authors.initials: H
authors.lastname: Xiao
authors.affiliation: The Australian School of Advanced Medicine, Macquarie University, 2 Technology Place, Macquarie Park, NSW 2113, Australia.
authors.forename: Alberto
authors.initials: A
authors.lastname: Avolio
authors.affiliation: Sichuan Mianyang 404 Hospital, No. 56 Yuejing Road, Fucheng District, Mianyang, Sichuan Province 400050, PR China.
authors.forename: Decai
authors.initials: D
authors.lastname: Huang
mesh_terms: D001158:Arteries / Q000473:pathology*; D003251:Constriction, Pathologic / Q000175:diagnosis*; D006801:Humans; D015233:Models, Statistical; D060388:Support Vector Machine; D014652:Vascular Diseases / Q000175:diagnosis*
keywords: Arterial tree; Artery stenosis; Stenosis prediction; Support vector machine; Transfer function; Transmission line model