Wavelet transform-based prediction of the likelihood of successful defibrillation for patients exhibiting ventricular fibrillation

Abstract

We report on an improved method for the prediction of the outcome from electric shock therapy for patients in ventricular fibrillation: the primary arrhythmia associated with sudden cardiac death. Our wavelet transform-based marker, COP (cardioversion outcome prediction), is compared to three other well-documented shock outcome predictors: median frequency (MF) of fibrillation, spectral energy (SE) and AMSA (amplitude spectrum analysis). Optimum specificities for sensitivities around 95% for the four reported methods are 63 ± 4% at 97 ± 2% (COP), 42 ± 15% at 90 ± 7% (MF), 12 ± 3% at 94 ± 5% (SE) and 56 ± 5% at 94 ± 5% (AMSA), with successful defibrillation defined as the rapid (<60 s) return of sustained (>30 s) spontaneous circulation. This marked increase in performance by COP at specificity values around 95%, required for implementation of the technique in practice, is achieved by its enhanced ability to partition pertinent information in the time–frequency plane. COP therefore provides an optimal index for the identification of patients for whom shocking would be futile and for whom an alternative therapy should be considered.

Prediction of Defibrillation Outcome by Ventricular Fibrillation Waveform Analysis: A Clinical Review

“…representing globally averaged information, is computed over the clipped ECG segment. The calculated features from the fast Fourier transform (FFT) of VF signals, such as peak power frequency (PF) or dominant frequency (DF) [36,[41][42][43][44][45], energy [38,46], maximum power (maximum value of the power spectral density (PSD)), power spectrum area (PSA), centroid power [38] , centroid frequency(FC) [38,44,46,47], median frequency(MF) [45,48,49], spectral flatness measure, fibrillation power, instantaneous mean frequency, frequency ratio and amplitude spectrum area (AMSA) [38], were shown to be capable of predicting defibrillation success.…”

Analysis of electrocardiogram pre-shock waveforms during ventricular fibrillation

“…Existing literature shows that frequency features derived from ECGs during VF change over time and this has been used to predict shock outcomes [9,32]. Entropy is another common feature in literature used to measure the amount of organization in the VF signals [17]. Therefore, these features were extracted from the ISs and the ability to discriminate between successful and unsuccessful shock outcomes was evaluated.…”

“…Entropy: This is a measurement of information content of a distribution [17] and is defined by Eq. (16).…”

“…Two such markers include scaling exponent (SE) and logarithm of absolute correlation (LAC) [13,14]. Wavelet analysis has also been proposed on the ECG during VF [15] and markers in the wavelet domain, such as cardioversion outcome prediction (COP) [16] and wavelet entropy [17], were introduced. Our preliminary works have also explored wavelet features and arrived at a relative scale distribution width (SDW) feature in predicting shock outcomes [18,19], expanding the application of this feature to quantify CPR efficacy by indirectly measuring coronary flow during VF [20].…”

New Device for Assessment of Autonomous Nervous System Functioning in Psychophysiology

“…Recent works show that the evaluation of autonomic nervous system (ANS) can be used both for diagnosis and outcome prediction of different chronic diseases [5,6,7] and as a marker of emotion regulation, behavioral predisposition and of certain aspects of psychological adjustment [8,9,10,11,12,13,14,15,16]. The analysis of heart rate variability (HRV) provides insight into the neural control of the heart in various real-life conditions.…”