GAUSSIAN PULSE DECOMPOSITION - AN INTUITIVE MODEL OF ELECTROCARDIOGRAM WAVE-FORMS

This study presents a novel approach to modeling the electrocardiogram (ECG): the Gaussian pulse decomposition. Constituent waves of the ECG are decomposed into and represented by Gaussian pulses using an itera tive algorithm: the chip away decomposition (ChAD) algorithm. At each iteration, a nonlinear minimization method is used to fit a portion of the ECG waveform with a single Gaussian pulse, which is then subtract ed from the ECG waveform. The process iterates on the resulting residu al waveform until the normalized mean square error is below an accepta ble level. Three different minimization methods were compared for thei r applicability to the ChAD algorithm; the Nelder-Mead simplex method was found to be more noise-tolerant than the Newton-Raphson method or the steepest descent method. Using morphologically different ECG wavef orms from the MIT-BIH arrhythmia database, it was demonstrated that th e ChAD algorithm is capable of modeling not only normal beats, but als o abnormal beats, including those exhibiting a depressed ST segment, b undle branch block, and premature ventricular contraction. An analytic al expression for the spectral contributions of the constituent waves was also derived to characterize the ECG waveform in the frequency dom ain. The Gaussian pulse model, providing an intuitive representation o f the ECG constituent waves by use of a small set of meaningful parame ters, should be useful for various purposes of ECG signal processing, including signal representation and pattern recognition.