Detection of ventricular fibrillation in the presence of cardiopulmonary resuscitation artefacts

Providing cardiopulmonary resuscitation (CPR) to a patient in cardiac arrest introduces artefacts into the electrocardiogram (ECG), corrupting the diagnosis of the underlying heart rhythm. CPR must therefore be discontinued for reliable shock advice analysis by an automated external defibrillator (AED). Detection of ventricular fibrillation (VF) during CPR would enable CPR to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. This study presents a new adaptive filtering method to clean the ECG. The approach consists of a filter that adapts its characteristics to the spectral content of the signal exclusively using the surface ECG that commercial AEDs capture through standard patches. A set of 200 VF and 25 CPR artefact samples collected from real out-of-hospital interventions were used to test the method. The performance of a shock advice algorithm was evaluated before and after artefact removal. CPR artefacts were added to the ECG signals and four degrees of corruption were tested. Mean sensitivities of 97.83%, 98.27%, 98.32% and 98.02% were achieved, producing sensitivity increases of 28.44%, 49.75%, 59.10% and 64.25%, respectively, sufficient for ECG analysis during CPR. Although satisfactory and encouraging sensitivity values have been obtained, further clinical and experimental investigation is required in order to integrate this type of artefact suppressing algorithm in current AEDs.     

Amplitude spectrum area to guide resuscitation—A retrospective analysis during out-of-hospital cardiopulmonary resuscitation in 609 patients with ventricular fibrillation cardiac arrest

Introduction

The capability of amplitude spectrum area (AMSA) to predict the success of defibrillation (DF) was retrospectively evaluated in a large database of out-of-hospital cardiac arrests.

Methods

Electrocardiographic data, including 1260 DFs, were obtained from 609 cardiac arrest patients due to ventricular fibrillation. AMSA sensitivity, specificity, accuracy, and positive and negative predictive values (PPV, NPV) for predicting DF success were calculated, together with receiver operating characteristic (ROC) curves. Successful DF was defined as the presence of spontaneous rhythm ≥40 bpm starting within 60 s from the DF. In 303 patients with chest compression (CC) depth data collected with an accelerometer, changes in AMSA were analyzed in relationship to CC depth.

Results

AMSA was significantly higher prior to a successful DF than prior to an unsuccessful DF (15.6 ± 0.6 vs. 7.97 ± 0.2 mV-Hz, p < 0.0001). Intersection of sensitivity, specificity and accuracy curves identified a threshold AMSA of 10 mV-Hz to predict DF success with a balanced sensitivity, specificity and accuracy of almost 80%. Higher AMSA thresholds were associated with further increases in accuracy, specificity and PPV. AMSA of 17 mV-Hz predicted DF success in two third of instances (PPV of 67%). Low AMSA, instead, predicted unsuccessful DFs with high sensitivity and NPV >97%. Area under the ROC curve was 0.84. CC depth affected AMSA value. When depth was <1.75 in., AMSA decreased for consecutive DFs, while it increased when the depth was >1.75 in. (p < 0.05).

Conclusions

AMSA could be a useful tool to guide CPR interventions and predict the optimal timing of DF.     

Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest

Aims

Repeated failed shocks for ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OOHCA) can worsen the outcome. It is very important to rapidly distinguish between early and late VF. We hypothesised that VF waveform analysis based on detrended fluctuation analysis (DFA) can help predict successful defibrillation.

Methods

Electrocardiogram (ECG) recordings of VF signals from automated external defibrillators (AEDs) were obtained for subjects with OOHCA in Taipei city. To examine the time effect on DFA, we also analysed VF signals in subjects who experienced sudden cardiac death during Holter study from PhysioNet, a publicly accessible database. Waveform parameters including root-mean-squared (RMS) amplitude, mean amplitude, amplitude spectrum analysis (AMSA), frequency analysis as well as fractal measurements including scaling exponent (SE) and DFA were calculated. A defibrillation was regarded as successful when VF was converted to an organised rhythm within 5 s after each defibrillation.

Results

A total of 155 OOHCA subjects (37 successful and 118 unsuccessful defibrillations) with VF were included for analysis. Among the VF waveform parameters, only AMSA (7.61 ± 3.30 vs. 6.30 ± 3.13, P = 0.028) and DFAα2 (0.38 ± 0.24 vs. 0.49 ± 0.24, P = 0.013) showed significant difference between subjects with successful and unsuccessful defibrillation. The area under the curves (AUCs) for AMSA and DFAα2 was 0.63 (95% confidence interval (CI) = 0.52-0.73) and 0.65 (95% CI = 0.54-0.75), respectively. Among the waveform parameters, only DFAα2, SE and dominant frequency showed significant time effect.

Conclusions

The VF waveform analysis based on DFA could help predict first-shock defibrillation success in patients with OOHCA. The clinical utility of the approach deserves further investigation.     

A new way to analyze resuscitation quality by reviewing automatic external defibrillator data

Aims

High quality cardiopulmonary resuscitation (CPR) plays an important role in survival of out-of-hospital cardiac arrests (OHCAs). We have developed an algorithm to automatically identify the quality of chest compressions from data retrieved from automatic external defibrillators (AEDs).

Methods

Electrocardiographic (ECG) signals retrieved from AEDs were analyzed by a newly developed algorithm to identify fluctuations in CPR. The algorithm contained three steps. First, it decomposed the AED signals into several intrinsic mode fluctuations (IMFs) by empirical mode decomposition (EMD). Second, it identified the dominant IMFs that carried the chest compression signals and weighted the IMFs to both enhance the chest compression oscillations and filter the noise. Third, it calculated the autocorrelation function (ACF) of the reconstructed signals and tested their periodicity. Using this algorithm, several CPR quality indicators were automatically calculated minute-by-minute and compared with those derived by audio and visual review of AED data by experienced physicians.

Results

A total of 77 (29 women, 48 men) OHCA patients were enrolled, and 351 one-min segments were analyzed. The results showed that the CPR quality parameters calculated from the algorithm were highly correlated with those from the manual review (all P < 0.001). The limits of agreement by Bland-Altman analysis were acceptable for chest compression number, total flow time, and no flow time, but not for CPR rate. We also demonstrated that only 41.8 ± 29.8% of time was spent in chest compressions and only 7.5 ± 16.8% was spent in adequate chest compressions.

Conclusion

Our results demonstrated that several indicators of CPR quality can be precisely and automatically determined by analyzing the ECG signals from AEDs using EMD and autocorrelograms.     

二维超声观察主动性心肺复苏对心室颤动犬心功能的影响

目的 探讨主动性心肺复苏对犬心室颤动(室颤)时心功能的影响.方法 应用经胸二维超声心动图分别测量犬室颤时主动性心肺复苏组(ACD-CPR组,12只)和标准心肺复苏组(S-CPR组,12只)左心室舒张末期容积、左心室收缩末期容积,计算左心室射血分数(LVEF),比较两组间各项测量结果.结果 室颤时,两组左心室舒张末期容积较室颤前均有下降,但差异均无统计学意义(P＞0.05);两组左心室收缩末期容积较室颤前显著增大,差异有统计学意义(P＜0.001).CPR时,ACD-CPR组比S-CPR组左室舒张末期容积增加,但差异无统计学意义(P＞0.05);前者比后者左室收缩末期容积减少,但差异无统计学意义(P＞0.05).CPR时,ACD-CPR组与S-CPR组LVEF较室颤前显著下降,差异有统计学意义(P＜0.001);前者LVEF较后者显著增加,差异有统计学意义(P=0.019).结论 主动性心肺复苏较常规心肺复苏有助于改善左心室功能.     

Effect and mechanism of esmolol given during cardiopulmonary resuscitation in a porcine ventricular fibrillation model

Objectives

The aim of the study was to investigate the effect on calcium cycling protein and electrical restitution of β₁-adrenergic receptor antagonist esmolol administered during cardiopulmonary resuscitation in the porcine ventricular fibrillation model.

Methods

Ventricular fibrillation untreated for four minutes was induced by dynamic steady state pacing protocol in 40 healthy male pigs, in which local unipolar electrograms were recorded using one 10-electrode catheter that was sutured to the left ventricular epicardium. During CPR, animals were randomized into two groups to receive saline as placebo or esmolol after two standard doses of epinephrine. At post-resuscitation 2-h, six pigs were randomly selected from each group and the second VF induction was performed. Local activation-recovery intervals (ARI) restitutions and the VF inducibility between control group and esmolol group were compared. Western blotting was performed to determine expression of Ca²⁺/calmodulin-dependent protein kinase IIδ(CaMKIIδ) and cardiac ryanodine receptor (RyR2) protein, and their phosphorylation status.

Results

Injection of esmolol combined with epinephrine during CPR significantly decreased recurrent rate of ventricular fibrillation during 2-h post-resuscitation, meanwhile it has no adverse affect on the restore of spontaneous circulation. Esmolol significantly flattened ARI restitution slope, lessened regional difference of ARI restitution, decreased the VF inducibility, and alleviated CaMKIIδ hyper-activation and RyR2 hyper-phosphorylation.

Conclusions

Esmolol given during CPR has significant effects on modulating electrical restitution property and intracellular calcium handling, which contributes the most important reasons why β₁-blockade significantly reduced the onset and maintenance of VF.     

自动体外除颤仪对猪心肺复苏的效果及对心功能的影响

目的 探讨自动体外除颤仪(AED)在抢救心搏骤停中的作用和应用方法,对比国产及进口AED的除颤和复苏效果.方法 14头北京长白猪,体质量(30±1)kg,于本院动物实验室,麻醉后左股静脉置入双腔临时起搏电极,连接医用程控刺激仪制作室颤模型.左股动脉置入动脉导管,连接PiCCO监护仪测量动脉血压及心输出量(CO)和肺血管外水指数(EVWI).心电监护证实室颤成功后,随机(随机数字法)将动物分为2组,每组7只,随机使用国产(M组)或进口(Z组)自动体外除颤仪(AED)除颤.胸骨两侧粘贴电极,按AED语音提示操作并除颤.以上过程反复进行4次,记录除颤次数及成功率.每次自主循环恢复(ROSC)后20 min进行心肌酶谱检测,同时监测CO及EVWI.实验数据计量资料采用重复测量方差分析,计数资料采用x2检验,以P＜0.05为差异有统计学意义.结果 14只动物共进行54次致颤,除Z组1只第二次致颤后未能成功复苏,其余均ROSC,复苏成功率为98.1%.M组放电37次,首次除颤成功率75.0%;Z组放电32次,首次除颤成功率80.8%.从AED开机到心电信号识别完毕平均需要(29±1)s.M组及Z组各2次在首次致颤后,AED未能成功识别室颤;Z组2次将ROSC后室性心动过速,误判为室颤,但按其提示除颤后未造成不良影响.实验过程中,所有动物心率、血压及CO未见明显波动,但EVWI和肌红蛋白(MYO)随时间变化进行性升高,其中第三、第四次除颤后与基础状态相比,有显著性差异.各项检测指标M组与Z组之间未发现显著性差异.结论 AED能够安全、有效地终止室颤;国产与进口AED在信号判别能力、除颤效果与对心肌损伤方面无显著差异;对于专业医护人员,推荐使用人工除颤器,以避免AED过度依赖自动化而产生的误判.     

Potassium induced cardiac standstill during conventional cardiopulmonary resuscitation in a pig model of prolonged ventricular fibrillation cardiac arrest: A feasibility study

Aim of the study

Potassium-based cardioplegia has been the gold standard for cardioprotection during cardiac surgery. We sought to evaluate the feasibility and the effects of potassium-induced cardiac standstill during conventional cardiopulmonary resuscitation (CPR) in a pig model of prolonged ventricular fibrillation (VF).

Methods

VF was induced in 20 pigs, and circulatory arrest was maintained for 14 min. Animals were then resuscitated by standard CPR. Coincident with the start of CPR, 20 ml of saline (control group) or 0.9 mequiv. kg⁻¹ of potassium chloride diluted to 20 ml (potassium group) was administered into right atrium.

Results

Administration of potassium resulted in asystole lasting for 1.0 min (0.2) in the potassium group animals. VF reappeared in all but one animal, in which wide QRS complex bradycardia followed. Restoration of spontaneous circulation (ROSC) was attained in two animals (20%) in the control group and in seven animals (70%) in the potassium group (p = 0.070). Resuscitated animals in the potassium group required fewer countershocks (3, 4 vs. 2 (1–2)), smaller doses of adrenaline (1.84, 1.84 vs. 0.94 (0.90–1.00) mg), and shorter duration of CPR (8, 10 vs. 4.0 (4.0–4.0) min) than did the control group. Potassium concentrations normalised rapidly after ROSC in both groups, and the potassium concentrations at 5 min (5.5, 6.6 vs. 6.8 (6.5–7.8) mequiv. l⁻¹) and 4 h (4.9, 5.4 vs. 5.9 (5.1–6.4) mequiv. l⁻¹) after ROSC were similar in the both groups.

Conclusion

In a pig model of untreated VF cardiac arrest for 14 min, resuscitation with potassium-induced cardiac standstill during conventional CPR was found to be feasible.     

Characteristics of ventricular fibrillation in relation to cardiac aetiology and shock success: A waveform analysis study in ICD-patients

BackgroundVentricular fibrillation (VF) waveform characteristics are associated with cardiac arrest duration and defibrillation success. Recent animal studies found that VF characteristics and shock success also depend on the presence of myocardial infarction (MI). In patients, VF induction after implantable cardioverter defibrillator (ICD) implantation offers a unique setting to study early VF characteristics: we studied the relation with cardiac disease – either presence or absence of a previous MI – and with shock success.MethodsRetrospective cohort study of ICD-patients who underwent defibrillation testing, 117 (63%) with and 69 (37%) without a previous MI. Intracardiac recordings of induced VF were analysed using Fourier analysis.ResultsIn previous MI-patients, the fundamental frequency and organisation index of the VF signal were significantly lower as compared with patients without a previous MI: 4.9 Hz ± 0.6 vs. 5.2 Hz ± 0.6 (p = 0.005) and 56% ± 10 vs. 60% ± 9 (p = 0.001), respectively. The median frequency was not different (p = 0.25). We found no association between VF characteristics and ICD shock success.ConclusionsIn analogy with observations in animals, we found that a history of a previous MI was associated with slower and less organised VF. In our cohort of ICD-patients, early VF waveform characteristics were not associated with shock outcomes. Further study is warranted to determine to what extent VF characteristics are influenced by the underlying aetiology on the one hand, and time delay on the other. These findings could improve insight into the potential value of VF analysis to guide shock delivery.     

Correlation between coronary perfusion pressure and quantitative ECG waveform measures during resuscitation of prolonged ventricular fibrillation

Introduction

The ventricular fibrillation (VF) waveform is dynamic and predicts defibrillation success. Quantitative waveform measures (QWMs) quantify these changes. Coronary perfusion pressure (CPP), a surrogate for myocardial perfusion, also predicts defibrillation success. The relationship between QWM and CPP has been preliminarily explored. We sought to further delineate this relationship in our porcine model and to determine if it is different between animals with/without ROSC (return of spontaneous circulation).

Hypothesis

A relationship exists between QWM and CPP that is different between animals with/without ROSC.

Methods

Utilizing a prior experiment in our porcine model of prolonged out-of-hospital VF cardiac arrest, we calculated mean CPP, cumulative dose CPP, and percent recovery of three QWM during resuscitation before the first defibrillation: amplitude spectrum area (AMSA), median slope (MS), and logarithm of the absolute correlations (LAC). A random effects linear regression model with an interaction term CPP*ROSC investigated the association between CPP and percent recovery QWM and how this relationship changes with/without ROSC.

Results

For 12 animals, CPP and QWM measures (except LAC) improved during resuscitation. A linear relationship existed between CPP and percent recovery AMSA (coefficient 0.27; 95%CI 0.23, 0.31; p < 0.001) and percent recovery MS (coefficient 0.80; 95%CI 0.70, 0.90; p < 0.001). A linear relationship existed between cumulative dose CPP and percent recovery AMSA (coefficient 2.29; 95%CI 2.0, 2.56; p < 0.001) and percent recovery MS (coefficient 6.68; 95%CI 6.09, 7.26; p < 0.001). Animals with ROSC had a significantly “steeper” dose–response relationship.

Conclusions

There is a linear relationship between QWM and CPP during chest compressions in our porcine cardiac arrest model that is different between animals with/without ROSC.     

Ventricular fibrillation waveform characteristics differ according to the presence of a previous myocardial infarction: A surface ECG study in ICD-patients

BackgroundCharacteristics of the ventricular fibrillation (VF) waveform reflect arrest duration and have been incorporated in studies on algorithms to guide resuscitative interventions. Findings in animals indicate that VF characteristics are also affected by the presence of a previous myocardial infarction (MI). As studies in humans are scarce, we assessed the impact of a previous MI on VF characteristics in ICD-patients.MethodsProspective cohort of ICD-patients (n = 190) with defibrillation testing at the Radboudumc (2010–2013). VF characteristics of the 12-lead surface ECG were compared between three groups: patients without a history of MI (n = 88), with a previous anterior (n = 47) and a previous inferior MI (n = 55).ResultsAs compared to each of the other groups, the mean amplitude and amplitude spectrum area were lower, for an anterior MI in lead V3 and for an inferior MI in leads II and aVF. Across the three groups, the bandwidth was broader in the leads corresponding with the infarct localisation. In contrast, the dominant and median frequencies only differed between previous anterior MI and no history of MI, being lower in the former.ConclusionsThe VF waveform is affected by the presence of a previous MI. Amplitude-related measures were lower and VF was less organised in the ECG-lead(s) adjacent to the area of infarction. Although VF characteristics of the surface ECG have so far primarily been considered a proxy for arrest duration and metabolic state, our findings question this paradigm and may provide additional insights into the future potential of VF-guided resuscitative interventions.     

The frequency ratio: an improved method to estimate ventricular fibrillation duration based on Fourier analysis of the waveform

BACKGROUND: CPR prior to defibrillation improves survival from prolonged ventricular fibrillation (VF) by over 10%, based on recent studies. Waveform analysis could identify those patients with VF of over 5 to 7-min duration who should receive CPR first. A method based on the changes in the Fourier-derived frequency spectrum over time could improve the ability to identify prolonged VF. METHODS AND RESULTS: The Fourier frequency spectra were calculated on 5-s epochs of VF from 45 swine for 12.5 min. The average power at each frequency shows a marked loss of frequencies above 8 Hz occurring at 5 min accompanied by an increase in the power in frequency spectra from 3 to 5 Hz. A measure termed the frequency ratio is defined as the ratio of the power in the high frequency band from 8 to 24 Hz compared to the power in the low frequency band from 3 to 5 Hz. The frequency ratio is shown to detect 90% of epochs with VF less than 5 min while allowing selection of 74% of those epochs over 5 min. When the frequency ratio is set to detect 90% of episodes of VF under 7 min, it is able to select 88% of those traces with VF over 7 min. The receiver operating curve (ROC) for the frequency ratio has an area under the curve of 0.91 at 5 min and 0.95 at 7 min of VF duration. CONCLUSIONS: The frequency ratio is an improved frequency-based measure of VF duration. Waveform analysis techniques could determine which patients should receive shock first and which should receive a period of CPR prior to shock, thereby increasing probability of survival.     

Effectiveness of the new 'Mobile AED Map' to find and retrieve an AED: A randomised controlled trial

Background

Although early shock with an automated external defibrillator (AED) is one of the several key elements to save out-of-hospital cardiac arrest (OHCA) victims, it is not always easy to find and retrieve a nearby AED in emergency settings. We developed a cell phone web system, the Mobile AED Map, displaying nearby AEDs located anywhere. The simulation trial in the present study aims to compare the time and travel distance required to access an AED and retrieve it with and without the Mobile AED Map.

Methods

Design: Randomised controlled trial. Setting: Two fields where it was estimated to take 2 min (120-170 m) to access the nearest AED. Participants were randomly assigned to either the Mobile AED Map group or the control group. We provided each participant in both groups with an OHCA scenario, and measured the time and travel distance to find and retrieve a nearby AED.

Results

Forty-three volunteers were enrolled and completed the protocol. The time to access and retrieve an AED was not significantly different between the Mobile AED Map group (400 ± 238 s) and the control group (407 ± 256 s, p = 0.92). The travel distance was significantly shorter in the Mobile AED Map group (606 m vs. 891 m, p = 0.019). Trial field conditions affected the results differently.

Conclusions

Although the new Mobile AED Map reduced the travel distance to access and retrieve the AED, it failed to shorten the time. Further technological improvements of the system are needed to increase its usefulness in emergency settings (UMIN000002043).