Scaling Structure of Electrocardiographic Waveform During Prolonged Ventricular Fibrillation in Swine

Abstract

Ventricular fibrillation (VF) is the most common arrhythmia causing sudden cardiac death. However, the likelihood of successful defibrillation declines with increasing duration of VF. Because the morphology of the electrocardiogram (ECG) waveform during VF also changes with time, this study examined a new measure that describes the VF waveform and distinguishes between early and late VF. Surface ECG recordings were digitized at 200 samples/s from nine swine with induced VF. A new measure called the scaling exponent was calculated by examining the power-law relationship between the summation of amplitudes of a 1,024-point (5.12 second) waveform segment and the time scale of measurement. The scaling exponent is a local estimate of the fractal dimension of the ECG waveform. A consistent power-law relationship was observed for measurement time scales of 0.005-0.040 seconds. Calculation of the scaling exponent produced similar results between subjects, and distinguished early VF (< 4-minute duration) from late VF (> or = 4-minute duration). The scaling exponent was dependent on the order of the data, supporting the hypothesis that the surface ECG during VF is a deterministic rather than a random signal. The waveform of VF results from the interaction of multiple fronts of depolarization within the heart, and may be described using the tools of nonlinear dynamics. As a quantitative descriptor of waveform structure, the scaling exponent characterizes the time dependent organization of VF.

The effect of ischemia on ventricular fibrillation as measured by fractal dimension and frequency measures

“…The scaling exponent (ScE), LAC, and Hurst exponent have been well characterized in a large group of swine in earlier experimental work [7,9,14]. They show an increase in the fractal dimension over time.…”

“…Earlier laboratory studies have demonstrated that VF waveform analysis methods can provide an estimate of the duration of VF from the recorded electrocardiogram for use in treatment algorithms [4][5][6][7][8][9][10]. These methods exploit the two main features of the VF waveform, namely, frequency and roughness.…”

“…Early VF which has large smooth contours has a dimension near 1.1 and this gradually increases with VF duration to a dimension of 1.5 or greater as the waveform becomes more irregular. [7,9] The three frequency based measures have been studied extensively and include median frequency, angular velocity, and frequency ratio [4][5][6][7][8]12,16]. Measures of the fractal dimension have also been developed to analyze VF and include the scaling exponent (ScE) [9,[13][14][15] and the logarithm of the absolute correlations (LAC) [17].…”

Influence of the skeletal muscle activity on time and frequency domain properties of the body surface ECG during evolving ventricular fibrillation in the pig

“…This measure shows how the signal under consideration differs from the random one, which is characterized by ScE≈2 [6]. We estimated ScE as described previously [1,3]. Briefly, we calculated function L (k) for lag k = 1−1024.…”

“…Many attempts have been made to use the measures based on the ECG waveform in frequency and time domain as a means of assessing VF duration for optimization of the resuscitation strategies [1][2][3][4][5][6][7][8][9][10][11]. Accumulated clinical and laboratory data [1][2][3][4][5][6][7][8][9][10][11] have shown that these ECG waveform-based measures, or combinations of these measures, can help to predict the likelihood of the successful countershock therapy in cardiac arrest patients. However, analysis of ECG during VF can be confounded by extra-cardiac phenomena.…”