Optimizing the Timing of Defibrillation: The Role of Ventricular Fibrillation Waveform Analysis During Cardiopulmonary Resuscitation

Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest

“…Although numerous algorithms have been developed and these measures provided encouraging results for optimizing the timing of defibrillation in both animal and clinical studies, there are still concerns that limit their implementation through clinical devices. For the methods using amplitude information, the recording conditions, movement artifact, recording devices, body habitus, electrode placement, and transthoracic impedance may alter measured VF features [ 30 , 31 ]. Even though frequency analysis to assess the VF waveform overcomes some of the problems encountered with amplitude analysis, the technique is suitable only for analysis of stationary signals where the waveform does not change.…”

A support vector machine for predicting defibrillation outcomes from waveform metrics

“…2 Recent efforts to improve defibrillation efficacy have focused on optimising the timing of defibrillation during cardiopulmonary resuscitation (CPR). [3][4][5] Weisfeldt and Becker proposed a more individualised approach to VF management and postulated that a CPR first strategy for prolonged VF would optimise myocardial perfusion and improve defibrillation success and survival. 6 However knowledge of the time elapsed following VF onset is challenging in the prehospital environment and subsequent clinical trial data provided conflicting results, emphasising the need for a more robust measurement of VF phase beyond arrest duration.…”

“…9 Other time and frequency domain measurements of VF have also shown promise in estimating VF duration, predicting shock success, return to organised rhythm and prognosticating on short term survival in retrospective studies. 10 Advanced ECG time-frequency metrics classifiers and optimisation processes using a combination of machine learning techniques such as neural networks, supervised learning and support vector machine (SVM), have been used for predicting spontaneous termination of paroxysmal atrial fibrillation and successful cardioversion outcome in patients with atrial fibrillation 11,12 for the diagnosis of coronary heart disease 13 and VF waveform recognition. 14 An early study used nonparametric classification of VF waveform spectral features for predicting return of spontaneous circulation (ROSC) as their particular definition of defibrillation outcome in OHCA patients.…”

Estimating the amplitude spectrum area of ventricular fibrillation during cardiopulmonary resuscitation using only ECG waveform

“…Real-time electrocardiogram (ECG) waveform analysis has been advocated for several decades as a potential decision-making tool to optimize CPR (6). The VF signal changes over time, and therefore quantitative measures can help estimate the duration of VF, predict the likelihood of successful defibrillation, and evaluate the effectiveness of CPR (7). Both animal and clinical studies have demonstrated that quantitative VF signal analysis can be used as a non-invasive tool to optimize the timing of defibrillation, and allow CPR to be tailored to each individual heart (8,9).…”

“…Typically, the highest value of AMSA is frequently observed at the onset of VF, and declines as time elapses without treatment. When effective CPR is provided in time, a higher AMSA value is usually achieved due to the myocardium regaining perfusion status (7,14). Therefore, real-time monitoring of AMSA may serve as a qualitycontrol for CPR, revealing whether or not the myocardial blood flow has improved and whether or not the heart is ready for defibrillation (14,15).…”