S. Suppappola et al., GAUSSIAN PULSE DECOMPOSITION - AN INTUITIVE MODEL OF ELECTROCARDIOGRAM WAVE-FORMS, Annals of biomedical engineering, 25(2), 1997, pp. 252-260
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.