Feasibility of a predictive algorithm for identifying characteristic events of cardiac activation: results of a model study

Kozmann, György; Cserjes, Z.; Tyšler, Milan; Tinova, M.; Turzová, M; Szathmary, Vavrinec

doi:10.1109/cic.1994.470064

Cited by 1 publication

(1 citation statement)

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“…The biophysical background of the second derivatives suggest that pattern changes might be due to slight differences in local intraventricular activation propagation velocities, causing shifts in the timing of collisions of activation wavelets originated in distinct points of the Purkinje fiber-endocardial interface. We have to emphasize that the above interpretation needs further confirmation, though previous numerical modeling studies support our hypothesis [18].…”

Section: Computational Experimentsmentioning

confidence: 49%

The use of the SPSA method in ECG analysis

Gerencsér

Kozmann

Vágó

et al. 2002

IEEE Trans. Biomed. Eng.

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Abstract-The classification, monitoring, and compression of electrocardiogram (ECG) signals recorded of a single patient over a relatively long period of time is considered. The particular application we have in mind is high-resolution ECG analysis, such as late potential analysis, morphology changes in QRS during arrythmias, -wave alternants, or the study of drug effects on ventricular activation. We propose to apply a modification of a classical method of cluster analysis or vector quantization. The novelty of our approach is that we use a new distortion measure to quantify the distance of two ECG cycles, and the class-distortion measure is defined using a min-max criterion. The new class-distortion-measure is much more sensitive to outliers than the usual distortion measures using average-distance. The price of this practical advantage is that computational complexity is significantly increased. The resulting nonsmooth optimization problem is solved by an adapted version of the simultaneous perturbation stochastic approximation (SPSA) method of [1]. The main idea is to generate a smooth approximation by a randomization procedure. The viability of the method is demonstrated on both simulated and real data. An experimental comparison with the widely used correlation method is given on real data.

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Section: Computational Experimentsmentioning

confidence: 49%