Prompt prediction of successful defibrillation from 1-s ventricular fibrillation waveform in patients with out-of-hospital sudden cardiac arrest

Abstract

Energy spectrum analysis based on CWT as short as a 1.0-s VF ECG waveform enables prompt and reliable prediction of successful defibrillation.

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

“…For instance, MSI and LAC are respectively proportional to VF-amplitude or its square (see Equations (4) and (6)). Non-linear indices insensitive to VF amplitude, such as those based on detrended fluctuation analysis [29] or ApEn estimates for standardized r values [24], report SE/SP values around 60%, in line with our results.…”

“…The present study has some limitations. First, the results are based on the retrospective analysis of data, and a prospective study would be needed to confirm the benefits of using entropy-based waveform analysis to improve VF therapy on OHCA patients; second, patient outcome data were missing in many of the cases, so the prediction of survival and survival with good neurological outcome based on entropy could not be assessed; finally, the cohort of patients was comparable or larger than that of many studies addressing shock outcome prediction [24,25,57,58], but still limited to draw conclusive evidence on the benefits of using entropy measures to guide VF therapy during OHCA. Our results should be confirmed on data from larger and independent patient cohorts.…”

“…For instance, as time from collapse increases, VF amplitude decreases [19], and its dominant frequency and waveform roughness (the term roughness in VF waveform analysis was coined by Callaway, Sherman and colleagues in their contributions to the application of indices based on non-linear dynamics to predict defibrillation success [20,21]) reflect the different phases of VF [21,22]. Consequently, a plethora of VF-waveform features have been proposed to predict defibrillation success and to optimize treatment decisions [23][24][25][26]. These quantitative measures include the classical amplitude, slope or spectral analyses of VF [23,27], but also features derived from non-linear dynamics, such as: fractal dimension [28], Hurst exponents [28], scaling exponents [20], detrended fluctuation analysis [29] or Poincare-plot analysis [30].…”

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

“…Amplitude, slope, and phase of VF are not only dependent on the duration of VF but are also affected by other factors: interference, physique, skin resistance, size and position of electrodes, lead ways, and recording conditions [13]. Additionally, the time domain methods do not utilize the temporal information to predict the defibrillation [40]. For these reasons, time-domain features or characteristics are probably crude predictors of defibrillation.…”

“…The total mid-band (3-10 Hz) energy spectrum analysis based on continuous wavelet transform was studied by Endoh and colleagues using logistic regression analysis to predict defibrillation [40]. Features from dual-tree complex wavelet domain were developed for defibrillation outcome prediction by Shandilya and colleagues [28].…”