Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design

OBJECTIVE

Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned.

METHODS

A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class.

RESULTS

The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators.

CONCLUSIONS

The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.

Frequency Variation of Ventricular Fibrillation May Help Predict Successful Defibrillation in a Rat Model of Cardiac Arrest

BACKGROUND

To evaluate whether the frequency variation of ventricular fibrillation (VF) helps to predict successful defibrillation in a rat model of cardiac arrest.

METHODS

VF was induced in rats followed by cardiopulmonary resuscitation and then defibrillation. The electrocardiographic signals of 30 rats with first-shock success were obtained from our previous animal experiments, and 300 rats without first-shock success were selected as control. The VF waveform immediately before the first defibrillation was analyzed.

RESULTS

Eighty-eight percentages of the frequency variations of an electrocardiogram (ECG) record falling in the range -9.5-9.5 Hz was selected with sensitivity of 0.8, specificity of 0.583, and area under curve (AUC) of 0.708. Compared with amplitude spectrum area (AMSA) (sensitivity = 0.767, specificity= 0.547, and AUC = 0.678), combining frequency variation and AMSA significantly increases the predictability with sensitivity of 0.933, specificity of 0.493, and AUC of 0.732 (p = 0.005).

CONCLUSIONS

The frequency variation of VF may serve a useful parameter to predict defibrillation success.

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

BACKGROUND

Quantitative measures of the ventricular fibrillation (VF) ECG waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation because compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize cardiopulmonary resuscitation interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study's primary outcome.

METHODS

Five-second VF ECG segments were collected with and without chest compressions before 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and 3 combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ≤2) using 460 training cases, and their performance evaluated using 691 independent test cases.

RESULTS

Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve ranging from 0.56 to 0.75 (median, 0.73) without chest compressions and from 0.53 to 0.75 (median, 0.69) with compressions ( P<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.73-0.78) and with compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.72-0.78; P=0.75 for difference).

CONCLUSIONS

VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared with compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest area under the receiver operating characteristic curve. Such machine learning models may, therefore, offer means to guide resuscitation during uninterrupted cardiopulmonary resuscitation.

Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s.

Signal integral for optimizing the timing of defibrillation

OBJECTIVE

The possibility of successful defibrillation decreases with an increased duration of ventricular fibrillation (VF). Futile electrical shocks are inversely correlated with myocardial contractile function and long-term survival. Previous studies have demonstrated that various ECG waveform analyses predict the success of defibrillation. This study investigated whether the absolute amplitude of pre-shock VF waveform is likely to predict the success of defibrillation.

METHODS

ECG recordings of 350 out-of-hospital cardiac arrest (OOHCA) patients were obtained from the automated external defibrillator (AED) and analyzed by the method of signal integral. Successful defibrillation was defined as organized rhythm with heart rate ≥40beat/min commencing within one min of post-shock period and persisting for a minimum of 30s.

RESULTS

Signal integral was significantly greater in successful defibrillation than unsuccessful defibrillation (81.76±32.3mV vs. 34.9±15.33mV, p<0.001). The intersection of the sensitivity and specificity curve provided a threshold value of 51mV. The corresponding values of sensitivity, specificity, positive predictive and negative predictive values for successful defibrillation were 90%, 86%, 80% and 93%, respectively. The receiver operator curve further revealed that signal integral predicted the likelihood of successful defibrillation (area under the curve=0.949).

CONCLUSIONS

Signal integral predicted successful electrical shocks on patients with ventricular fibrillation and have potential to optimize the timing of defibrillation and reduce the number of electrical shocks.

Part 8: adult advanced cardiovascular life support: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care

The goal of therapy for bradycardia or tachycardia is to rapidly identify and treat patients who are hemodynamically unstable or symptomatic due to the arrhythmia. Drugs or, when appropriate, pacing may be used to control unstable or symptomatic bradycardia. Cardioversion or drugs or both may be used to control unstable or symptomatic tachycardia. ACLS providers should closely monitor stable patients pending expert consultation and should be prepared to aggressively treat those with evidence of decompensation.

Waveform analysis of ventricular fibrillation to predict defibrillation

PURPOSE OF REVIEW

Ventricular fibrillation occurs during many cases of cardiac arrest and is treated with rescue shocks. Coarse ventricular fibrillation occurs earlier after the onset of cardiac arrest and is more likely to be converted to an organized rhythm with pulses by rescue shocks. Less organized or fine ventricular fibrillation occurs later, has less power concentrated within narrow frequency bands and lower amplitude, and is less likely to be converted to an organized rhythm by rescue shocks. Quantitative analysis of the ventricular fibrillation waveform may distinguish coarse ventricular fibrillation from fine ventricular fibrillation, allowing more appropriate delivery of rescue shocks.

RECENT FINDINGS

A variety of studies in animals and humans indicate that there is underlying structure within the ventricular fibrillation waveform. Highly organized or coarse ventricular fibrillation is characterized by large power contributions from a few component frequencies and higher amplitude. Amplitude, decomposition into power spectra, or probability-based, nonlinear measures all can quantify the organization of human ventricular fibrillation waveforms. Clinical data have accumulated that these quantitative measures, or combinations of these measures, can predict the likelihood of rescue shock success, restoration of circulation, and survival to hospital discharge.

SUMMARY

Many quantitative ventricular fibrillation measures could be implemented in current generations of monitors/defibrillators to assist the timing of rescue shocks during clinical care. Emerging data suggest that a period of chest compressions or reperfusion can increase the likelihood of successful defibrillation. Therefore, waveform-based prediction of defibrillation success could reduce the delivery of failed rescue shocks.