Model-based analysis of the ECG during early stages of ventricular fibrillation

Frequency modulation within electrocardiograms during ventricular fibrillation

Periods of reentrant activation and effective refractory periods are correlated with dominant frequency or reciprocal of cycle periods during ventricular fibrillation (VF). In the present study, we used an analysis technique based on Wigner transforms to quantify time-varying dominant frequencies in electrocardiograms (ECGs) during VF. We estimated dominant frequencies within orthogonal ECGs recorded in 10 dogs during trials of 10 s of VF and in 9 dogs during trials of 30 s of VF. In four additional dogs, we compared dominant frequencies during 10 s of VF before and after administration of amiodarone. Our results showed the following. 1) There was substantial frequency variation or modulation within the ECGs during 10 and 30 s of VF, the average variation being +/-15% from the mean frequency. Amiodarone decreased mean frequencies (P < 0.05) as expected; however, amiodarone also decreased the variation in frequencies (P < 0.05). 2) During 30 s of VF, the dominant frequencies increased continuously from 7.3 to 8.1 Hz (P < 0.05). The increase in frequency was almost linear with a rate of 0.022 Hz/s (r(2) = 0.93, P < 0.0005). 3) Modulation of frequencies during the first and the last one-half of 30 s of VF was not different. Average (in time) mean frequencies and modulation of frequencies were similar in all three ECGs. 4) Although the averages were similar, during any VF episode, dominant frequencies in ECGs recorded from different locations on the body surface were similar to each other at some times and markedly different from each other at other times. We conclude that during VF, 1) frequencies in ECGs vary considerably and continuously, and amiodarone decreases this variation; 2) mean frequencies increase linearly during first 30 s; 3) the variability in frequency does not change during 30 s; and 4) at any given time, the frequencies within spatially different body surface ECGs can be either similar or markedly different.

Modeling the relationship between concurrent epicardial action potentials and bipolar electrograms

A signal analysis approach to building the relationship between concurrent epicardial cell action potentials (AP's) and bipolar electrograms is presented. Wavelet network, one nonlinear black-box modeling method, is used to identify the relationship between cell AP's and bipolar electrocardiograms. The electrical signals were simultaneously measured from the epicardium of isolated Langendorff-perfused rabbit hearts during three different rhythm conditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (NSRI), and ventricular fibrillation (VF). For NSR and NSRI, the proposed modeling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF. This result suggests that a time-invariant nonlinear modeling method such as wavelet network is not appropriate for VF rhythm, which is thought to be time-varying as well as chaotic, but still useful in detection of VF. A new arrhythmia detection algorithm, with potential application in implantable devices, is proposed for identifying the time of rhythmic bifurcation.