Efficient ECG Modeling using Polynomial Functions

摘要

ECG signal represents electrical changes on the skin that are caused by the heart muscles, and is usually measured by the electrodes placed on body surface. ECG analysis is one of the most common procedures in the heart diagnostic domain. After digitalization of ECG signal, next step in computer abstraction of ECG signal is appropriate modeling. Due to high importance of accurate modeling several ECG modeling approaches applicable for different purposes like heartbeat synthesis, analysis, compression, and filtering were introduced. In [1], a realistic ECG model designed for ECG synthesis based on Gausses curves modeling of appropriate waves is presented. Fitting this model to a real ECG signal is possible using iterative nonlinear optimization algorithms. An algorithm for ECG compression by alignment of heart beats in a matrix and fitting polynomial curves to columns is used by Riad B. [2]. Instantaneous module and the instantaneous phase of Hilbert Transform are used for ECG modeling in [3]. Mealy and Moore automata model is used in [4] for ECG complexity analysis.