Improving EASI Model via Machine Learning Techniques and Regression Techniques
E. Joseph and D. Bronzino. “Principles of Electrocardiography”, The Biomedical Engineering Handbook: Second Edition. 2000.
Bioelectromagnetism [Web page]. http://www.bem.fi/book/.
I. Tomasic and R. Trobec, “Electrocardiographic Systems With Reduced Numbers of Leads - Synthesis of the 12-lead ECG”, Biomedical Engineering, IEEE Reviews in Biomedical Engineering, vol.7, pp.126- 142, 2014.
G. E. Dower, “A Lead Synthesizer for the Frank System to Simulate the Standard 12-lead Electrocardiogram”, Journal of Electrocardiology, vol. 1, no. 1, pp. 101–116, 1968.
E. Frank, “An Accurate, Clinically Practical System for Spatial Vectorcardiography”, Circulation, vol. 13, no. 5, pp. 737–749, May 1956.
G. E. Dower, et al, “Deriving the 12-lead Electrocardiogram from four (EASI) Electrodes,” Journal of Electrocardiology, Vol.21, Supplement, pp.S182-S187, 1988.
D. Field, et al., “Improved EASI Coefficients: Their Derivation, Values and Performance”, Journal of Electrocardiology, pp.23-33, vol. 35, Supplement, 2002.
W. Oleksy, E. Tkacz and Z. Budzianowski, “Investigation Of A Transfer Function Between Standard 12-Lead ECG and EASI ECG”, BIOSIGNAL, Analysis of Biomedical Signals and Images, pp. 322-327, 2010.
W. Oleksy, E. Tkacz and Z. Budzianowski, “Improving EASI ECG Method Using Various Machine Learning and Regression Techniques to Obtain New EASI ECG Model”, Intl. Journal of Computer and Communication Engineering, vol. 1, no. 3, pp. 287-289, 2012.
W. Oleksy, E. Tkacz and Z. Budzianowski, “Improved EASI ECG Method As a Future Tool in Diagnostics of Patients Suffering from Noncommunicable Diseases”, Experimental & Amp, Clinical Cardiology, vol. 20, pp. 3663–3667, 2014.
Physionet Database. [Web page]
Linear Regression [Web page].
Polynomial Regression. [Web page]
P. Kaewfoongrungsi and D. Hormdee, “Deriving the 12-lead ECG from EASI Electrodes via Nonlinear Regression”, Intl. Journal of Advances in Electronics and Computer Science, vol.2, pp.106-110, 2015.
A. Yodjaiphet, N. Theera-Umpon and S. Auephanwiriyakul, “Electrocardiogram Reconstruction using Support Vector Regression”, Signal Processing and Information Technology (ISSPIT), IEEE International Symposium, pp.000269-000273, 2012.
M. Zavvar, et al., “Classification of Risk in Software Development Projects using Support Vector Machine”, Journal of Telecommunication, Electronic and Computer Engineering, pp. 1-5, vol. 9 no. 1, 2017.
P. Kaewfoongrungsi and D. Hormdee, “Deriving the 12-lead ECG from an EASI-lead System via Support Vector Regression,” KKU Engineering Journal, pp.317-322, 2016.
K. Priandana and B. Kusumoputro, “Development of Self-organizing Maps Neural Networks Based Control System for a Boat Model”, Journal of Telecommunication, Electronic and Computer Engineering, pp. 47-52, vol. 9, no. 1-3, 2017.
M. Talib, M. Othman and N. Azli, “Classification of Machine Fault using Principle Component Analysis, General Regression Neural Network and Probabilistic Neural Network”, Journal of Telecommunication, Electronic and Computer Engineering, pp. 93-98, vol. 8, no. 11, 2016.
H. Atoui, et al., “A Novel Neural-network Model for Deriving Standard 12-Lead ECGs from Serial Three-lead ECGs: Application to Self-care”, IEEE Transactions on Information Technology in Biomedicine, vol.14, No.3, pp.883-890, 2010.
T. Chai and R. Draxler, “Root Mean Square Error (RMSE) or Mean Absolute Error (MAE) – Arguments Against Avoiding RMSE in the Literature”, Geosciences Model Development, pp. 1247–1250, 2014.
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