2012 ©
             Publication
Journal Publication
Title of Article Improving EASI Model via Machine Learning and Regression Techniques 
Date of Acceptance 27 March 2018 
Journal
     Title of Journal Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 
     Standard SCOPUS 
     Institute of Journal Universiti Teknikal Malaysia Melaka (UTeM) 
     ISBN/ISSN 2289-8131 
     Volume 2018 
     Issue 10 
     Month April
     Year of Publication 2018 
     Page 115-120 
     Abstract We propose an approach to the interpretation of natural 12-lead Electrocardiography (ECG) is the standard tool for heart disease diagnose but measuring all 12 leads is often awkward and restricted by patient movement. In 1988, Gordon Dower has introduced the EASI-lead monitoring system that can reduce the number of electrodes from 10 downto 5 and also increases mobility of patients. In order to gain all 12-lead ECG back from the EASI-lead system, Dower’s equation was proposed then. Ever since various attempts have been explored to improve the synthesis accuracy. To find the best transfer function for synthesizing the 12-lead ECG from EASI-lead system, this paper presents a number of Machine Learning techniques including Support Vector Regression (SVR) and Artificial Neural Network (ANN). The experiments were conducted to compare the results from those Machine Learning methods to those of Linear Regression, Polynomial Regression, and Dower’s methods. The results have shown that the best performance amongst those methods with the least Root Mean Square Error (RMSE) values were obtained by SVR using spherical kernel function followed ANN, 3rd-order Polynomial Regression, Linear Regression and Dower’s equation, respectively. 
     Keyword 12-Lead ECG System, ANN, Dower's Method, EASI Electrodes, Linear Regression, Polynomial Regression, SVR 
Author
567040042-0 Mr. PIROON KAEWFOONGRUNGSI [Main Author]
Engineering Doctoral Degree

Reviewing Status มีผู้ประเมินอิสระ 
Status ตีพิมพ์แล้ว 
Level of Publication นานาชาติ 
citation true 
Part of thesis true 
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