Analysis of the Ventricular Fibrillation ECG Signal Amplitude and Frequency Parameters as Predictors of Countershock Success in Humans

Strohmenger, Hans-Ulrich; Lindner, Karl H.; Brown, Charles G.

doi:10.1378/chest.111.3.584

Cited by 140 publications

(73 citation statements)

References 16 publications

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“…[12][13][14][15][16][17] Various properties of the VF waveform have been shown to be strongly predictive of response to defibrillation. [18][19][20][21] Correlations between VF waveform properties and survival have also been shown. 22,23 For this reason, the utility of waveform analysis for the guided management of VF has been suggested.…”

Section: Circulationmentioning

confidence: 90%

Waveform Analysis–Guided Treatment Versus a Standard Shock-First Protocol for the Treatment of Out-of-Hospital Cardiac Arrest Presenting in Ventricular Fibrillation

et al. 2013

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Background-Ventricular fibrillation (VF) waveform properties have been shown to predict defibrillation success and outcomes among patients treated with immediate defibrillation. We postulated that a waveform analysis algorithm could be used to identify VF unlikely to respond to immediate defibrillation, allowing selective initial treatment with cardiopulmonary resuscitation in an effort to improve overall survival. Methods and Results-In a multicenter, double-blind, randomized study, out-of-hospital cardiac arrest patients in 2 urban emergency medical services systems were treated with automated external defibrillators using either a VF waveform analysis algorithm or the standard shock-first protocol. The VF waveform analysis used a predefined threshold value below which return of spontaneous circulation (ROSC) was unlikely with immediate defibrillation, allowing selective treatment with a 2-minute interval of cardiopulmonary resuscitation before initial defibrillation.

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Section: Circulationmentioning

confidence: 90%

Waveform Analysis–Guided Treatment Versus a Standard Shock-First Protocol for the Treatment of Out-of-Hospital Cardiac Arrest Presenting in Ventricular Fibrillation

et al. 2013

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“…The human heart fibrillates at frequencies that overlap with the characteristic frequencies of CPR artifacts [11], which are determined by the chest compression rate. This is one reason why CPR artifact removal presents a sophisticated signal-processing problem.…”

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confidence: 99%

Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals

Rheinberger

Steinberger

Unterkofler

et al. 2008

IEEE Trans. Biomed. Eng.

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“…Median frequency (MF) of the power spectrum is the frequency for which the integrated signal power was one half the total integrated power from 2 to 250 Hz [6,11]. We also computed function F lim (p) which estimated the upper limit of the frequency range containing p percent of the total integrated power in the range from 2 to 250 Hz.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2008

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Aim of the study-To evaluate influence of the skeletal muscle activity (SMA) on time and frequency domain properties of ECG during VF.Materials and methods-We studied the first 9 minutes of electrically induced VF (N=7). We recorded Lead II ECG, 247 unipolar epicardial ventricular electrograms (UEGs) and 3 bipolar skeletal electromyograms (EMGs) near the positions of the ECG electrodes (sampling rate, 500Hz). We reconstructed ECG (RECG) from UEGs using forward-solution transformation matrix. Spectral properties of ECG, RECG, UEGs and MEGs were assessed in the range 2−250Hz by the median frequency (MF) and the upper limit of frequency range containing 99% of spectral energy (F lim (99)). Scaling exponent of ECG, RECG and EMGs was calculated in the ranges of 1−8 and 5−20 sampling intervals (ScE 1−8 and ScE 5−20 , respectively). Conclusion-SMA significantly contributes to ECG during VF and can bias metrics used for assessment of VF organization. Results-We observed non-monotonic increases in MF and

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Analysis of the Ventricular Fibrillation ECG Signal Amplitude and Frequency Parameters as Predictors of Countershock Success in Humans

Cited by 140 publications

References 16 publications

Waveform Analysis–Guided Treatment Versus a Standard Shock-First Protocol for the Treatment of Out-of-Hospital Cardiac Arrest Presenting in Ventricular Fibrillation

Waveform Analysis–Guided Treatment Versus a Standard Shock-First Protocol for the Treatment of Out-of-Hospital Cardiac Arrest Presenting in Ventricular Fibrillation

Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals

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

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