Optimizing timing of ventricular defibrillation

Marn-Pernat, Andreja; Weil, Max Harry; Tang, Wanchun; Pernat, Andrej; Bisera, Joe

doi:10.1097/00003246-200112000-00019

Cited by 124 publications

(76 citation statements)

References 29 publications

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“…AMSA is a frequency weighted mean of the spectral amplitudes of the VF waveform [27], and it is the most extensively studied of all predictors [12]. To compute AMSA, the signal was Hamming windowed, and its fast Fourier transform (FFT), X(k), was computed using a N FFT = 2048 point FFT.…”

Section: Classical Shock Outcome Predictorsmentioning

confidence: 99%

“…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]. These later approaches suggest that quantifying the VF waveform regularity and/or predictability through non-linear metrics may be an adequate way to predict defibrillation success.…”

Section: Introductionmentioning

confidence: 99%

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

Chicote

Irusta

Alcaraz

et al. 2016

Entropy

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Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 µV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.

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Section: Classical Shock Outcome Predictorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chicote

Irusta

Alcaraz

et al. 2016

Entropy

View full text Add to dashboard Cite

show abstract

“…In a porcine model, the median frequency (CF correspondent) improved with CPR with no significant change in the amplitude (ENRG correspondent). 8 In other pig models, both Marn-Pernat et al 11 and Povoas et al 12 reported that AMSA improved with CPR. Marn-Pernat et al 11 demonstrated that both AMSA and ROSC increased with an increased duration of CPR.…”

Section: Discussionmentioning

confidence: 97%

“…The amplitude spectrum relationship (AMSA) emphasizes the high-frequency characteristics of the waveform by computing the sum of each frequency component amplitude weighted by the corresponding frequency. 11,12 Centroid frequency (CF) quantifies the frequency characteristics of the waveform by computing the frequency corresponding to the point of mass in the spectrum. 9 CF emphasizes the low-frequency content of VF.…”

Section: Calculating and Evaluating Predictors Of Roscmentioning

confidence: 99%

“…8 -12 This has previously been shown in animal experiments. 8,11,12 Therefore, the present analysis evaluates VF waveforms during periods of CPR in ECGs taken from patients in the study by Wik et al 4 We used previously published predictors of defibrillation success to quantify waveform changes. 9,[13][14][15] Because their predictive capabilities vary and not all of them have been reported on with human data, this aspect was evaluated in a separate data set.…”

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confidence: 99%

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Effects of Cardiopulmonary Resuscitation on Predictors of Ventricular Fibrillation Defibrillation Success During Out-of-Hospital Cardiac Arrest

2004

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Background-Early defibrillation is considered the most important factor for restoring spontaneous circulation in cardiac arrest patients with ventricular fibrillation. Recent studies have shown that, after prolonged ventricular fibrillation, the rates of return of spontaneous circulation (ROSC) and survival are improved if defibrillation is delayed so that CPR can be given first. To examine whether CPR improves myocardial readiness for defibrillation, we analyzed whether CPR causes changes in predictors of defibrillation success calculated from the ventricular fibrillation waveform. Methods and Results-ECG recordings were retrieved for 105 patients from an original study of 200 patients receiving CPR or defibrillation first. Altogether, 267 CPR sequences from 77 patients were identified on which the effect of CPR could be evaluated. Five predictors of ROSC (spectral flatness measure, energy, centroid frequency, amplitude spectrum relationship, and estimated probability of ROSC) were determined from a spectral analysis of the ventricular fibrillation waveform immediately before and immediately after each of the 267 sequences. CPR increased spectral flatness measure, centroid frequency, and amplitude spectrum relationship (PϽ0. 05, PϽ0.001, PϽ0.01). In an analysis of the effect of the duration of CPR, the probability of ROSC and amplitude spectrum relationship showed a positive change for CPR sequences lasting Ͼ3 minutes (PϽ0.001, PϽ0.05). Conclusions-During

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Quality of Cardiopulmonary Resuscitation During Out-of-Hospital Cardiac Arrest

Wik¹,

Kramer‐Johansen

Myklebust

et al. 2005

JAMA

1,253

578

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Optimizing timing of ventricular defibrillation

Abstract: AMSA has the potential for guiding more optimal timing of defibrillation without adverse interruption of cardiopulmonary resuscitation or the delivery of unsuccessful high energy electrical shocks that contribute to postresuscitation myocardial injury.

Cited by 124 publications

References 29 publications

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

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

Effects of Cardiopulmonary Resuscitation on Predictors of Ventricular Fibrillation Defibrillation Success During Out-of-Hospital Cardiac Arrest

Quality of Cardiopulmonary Resuscitation During Out-of-Hospital Cardiac Arrest

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