Universal Fluctuations in Very Short ECG Episodes

Abstract

We propose a new algorithm for the detection of ventricular fibrillation (VF) in very short surface electrocardiogram (ECG) episodes. Ventricular fibrillation is the most commonly identified arrhythmia in cardiac arrest patients and can lead to syncope, within seconds. The fast detection of ventricular fibrillation is necessary for prompt defibrillation either with an implantable cardioverter/defibrillator or an automated external defibrillator. Ventricular fibrillation generates stochastic waveforms and recently it has been shown that it exhibits characteristics similar to a non-chaotic signal and contains determinism Probability Density Function (PDF), for the different physical fluctuations was described previously. Accordingly, we describe scaling properties of very short shockable, VF and non-shockable ECG episodes and show that a universal PDF exists for the fluctuations of shockable ECG episodes. We compared the proposed algorithm with nine standard VF detection algorithms. The comparison indicated that our algorithm consistently produced more accurate detection results, then with standard algorithm. We conclude that the proposed method, based on fluctuation analysis, provides new information on the dynamics underlying VF, and allows a better detection compared to other algorithms.