Rhythm analysis during cardiopulmonary resuscitation: past, present, and future

The impact of time to defibrillation on return of spontaneous circulation in out-of-hospital cardiac arrest patients with recurrent shockable rhythms

OBJECTIVE

Optimal timing for subsequent defibrillation attempts for Out-of-hospital cardiac arrest (OHCA) patients with recurrent VF/pVT is uncertain. We investigated the relationship between VF/pVT duration and return of spontaneous circulation (ROSC) in OHCA patients with recurrent shockable rhythms.

METHODS

We analyzed data from the Salt Lake City Fire Department (SLCFD) spanning from 2012 to 2023. The implementation of rhythm-filtering technology since 2011 enabled real-time rhythm interpretation during CPR, with local protocols allowing early defibrillation for recurrent/refractory VF/pVT cases. We included patients experiencing four or five episodes of VF and pVT rhythms and employed generalized estimating equation (GEE) regression analysis to examine the association between VF/pVT durations preceding recurrent defibrillation and return of spontaneous circulation (ROSC).

RESULTS

Analysis of 622 appropriate shocks showed that patients achieving ROSC had significantly shorter median VF/pVT duration than those who did not achieve ROSC (0.83 minutes vs. 1.2 minutes, p = 0.004). Adjusted analysis of those with 4 VF/pVT episodes (N = 142) revealed that longer VF/pVT durations were associated with lower odds of achieving ROSC (odds ratio: 0.81, 95% CI: 0.72-0.93, p = 0.005). Every one-minute delay in intra-arrest defibrillation is predicted to decrease the likelihood of achieving ROSC by 19%.

CONCLUSION

Every one-minute increase in intra-arrest VF/pVT duration was associated with a statistically significant 19% decrease in the chance of achieving ROSC. This highlights the importance of reducing time to shock in managing recurrent VF/pVT. The findings suggest reevaluating the current recommendations of two minutes intervals for rhythm check and shock delivery.

Real-time amplitude spectrum area estimation during chest compression from the ECG waveform using a 1D convolutional neural network

Introduction: Amplitude spectrum area (AMSA) is a well-established measure than can predict defibrillation outcome and guiding individualized resuscitation of ventricular fibrillation (VF) patients. However, accurate AMSA can only be calculated during cardiopulmonary resuscitation (CPR) pause due to artifacts produced by chest compression (CC). In this study, we developed a real-time AMSA estimation algorithm using a convolutional neural network (CNN). Methods: Data were collected from 698 patients, and the AMSA calculated from the uncorrupted signals served as the true value for both uncorrupted and the adjacent corrupted signals. An architecture consisting of a 6-layer 1D CNN and 3 fully connected layers was developed for AMSA estimation. A 5-fold cross-validation procedure was used to train, validate and optimize the algorithm. An independent testing set comprised of simulated data, real-life CC corrupted data, and preshock data was used to evaluate the performance. Results: The mean absolute error, root mean square error, percentage root mean square difference and correlation coefficient were 2.182/1.951 mVHz, 2.957/2.574 mVHz, 22.887/28.649% and 0.804/0.888 for simulated and real-life testing data, respectively. The area under the receiver operating characteristic curve regarding predicting defibrillation success was 0.835, which was comparable to that of 0.849 using the true value of the AMSA. Conclusions: AMSA can be accurately estimated during uninterrupted CPR using the proposed method.

simulação da reanimação cardiopulmonar e o conhecimento de socorristas

Objetivo: comparar o conhecimento de socorristas antes e depois da capacitação de reanimação cardiopulmonar com o método da simulação realística. Método: estudo quase-experimental realizado com 41 socorristas do Suporte Básico de Vida que contemplam 8 bases da 18° Regional de Saúde do Paraná. Os socorristas responderam ao Instrumento para Avaliação da capacitação em Ressuscitação Cardiopulmonar aplicado antes e depois da simulação realística. Resultados: obteve-se diferença estatisticamente significativa (p < 0,02) em 6 das 10 questões, as quais abordaram: a sequência das manobras de reanimação cardiopulmonar; a carga elétrica do desfibrilador externo automático; a posição, a profundidade e a velocidade das compressões torácicas; a relação compressão/ventilação; e o manuseio do desfibrilador externo automático. Já em outras duas questões — reconhecimento da parada cardiorrespiratória e dispositivo de ventilação com pressão positiva — não houve mudança quanto à alternativa de resposta. Encontraram-se 60% de acertos das questões quando avaliado o conhecimento prévio e 90% de acertos após as fases da simulação realística. Conclusão: os socorristas não atingiram com totalidade o questionário de pré-teste; entretanto, após a estratégia da simulação realística, houve um aumento significativo desse conhecimento. Esses resultados demonstraram melhoria no conhecimento cognitivo dos socorristas após simulação, o que foi comprovado pelo aumento de conhecimento expresso no pós-teste. Essa metodologia também pode ser aplicada com sucesso a essa categoria profissional.

A review of progress and an advanced method for shock advice algorithms in automated external defibrillators

Shock advice algorithm plays a vital role in the detection of sudden cardiac arrests on electrocardiogram signals and hence, brings about survival improvement by delivering prompt defibrillation. The last decade has witnessed a surge of research efforts in racing for efficient shock advice algorithms, in this context. On one hand, it has been reported that the classification performance of traditional threshold-based methods has not complied with the American Heart Association recommendations. On the other hand, the rise of machine learning and deep learning-based counterparts is paving the new ways for the development of intelligent shock advice algorithms. In this paper, we firstly provide a comprehensive survey on the development of shock advice algorithms for rhythm analysis in automated external defibrillators. Shock advice algorithms are categorized into three groups based on the classification methods in which the detection performance is significantly improved by the use of machine learning and/or deep learning techniques instead of threshold-based approaches. Indeed, in threshold-based shock advice algorithms, a parameter is calculated as a threshold to distinguish shockable rhythms from non-shockable ones. In contrast, machine learning-based methods combine multiple parameters of conventional threshold-based approaches as a set of features to recognize sudden cardiac arrest. Noticeably, those features are possibly extracted from stand-alone ECGs, alternative signals using various decomposition techniques, or fully augmented ECG segments. Moreover, these signals can be also used directly as the input channels of deep learning-based shock advice algorithm designs. Then, we propose an advanced shock advice algorithm using a support vector machine classifier and a feature set extracted from a fully augmented ECG segment with its shockable and non-shockable signals. The relatively high detection performance of the proposed shock advice algorithm implies a potential application for the automated external defibrillator in the practical clinic environment. Finally, we outline several interesting yet challenging research problems for further investigation.

Impact of cardiopulmonary resuscitation duration on the neurological outcomes of out-of-hospital cardiac arrest

BACKGROUND

Patients experiencing cardiac arrest outside medical facilities are at greater risk of death and might have negative neurological outcomes. Cardiopulmonary resuscitation duration affects neurological outcomes of such patients, which suggests that duration of CPR may be vital to patient outcomes.

OBJECTIVES

The study aims to evaluate the impact of cardiopulmonary resuscitation duration on neurological outcome of patients who have suffered out-of-hospital cardiac arrest.

METHODS

This is a quantitate cross-sectional study where data is collected from emergency cases handled by a secondary hospital in industrial Jubail, Saudi Arabia, between January 2015 and December 2020. There were 257 out-of-hospital cardiac arrest cases, 236 of which resulted in death. The outcome is the survival of OHCA or death, and the neurological outcome by the cerebral performance category (CPC) score for survivors. A score of 1 or 2 defined as good CPC outcome and 3, 4, and 5 as poor outcome.

RESULTS

The mean for the duration of emergency CPR procedures in surviving patients is 26.5 ± 7.20 min, whereas in patients who died after the procedure it is 29.6 ± 9.15 min. Bivariate analysis showed no significant association between duration of CPR and Cerebral Performance Category (CPC) outcome but could be significant if the sample size is large. Age, however, is significantly related to the survivorship of OHCA and to a better CPC outcome. Younger patients are more likely to have better CPC outcome. A good CPC outcome was reported with a limited duration of 8.1 min of CPR, whereas, poor CPC outcomes were associated with prolonged periods of CPR, 13.2 min.

CONCLUSION

Cardiopulmonary Resuscitation Duration out-of-hospital cardiac arrest does not significantly influence the patient neurological outcome in the current study hospital. Variables such as the patient population's uniqueness, underlying medical conditions, or the specific study conditions may explain this variance between the bivariate analysis and the study conclusion. Therefore, a more comprehensive study is recommended in future.

Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR

Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed a condition-based filtering algorithm that consists of three stop-band filters which are turned either 'on' or 'off' depending on the ECG's spectral characteristics. Typically, removing the artifact's higher frequency peaks in addition to the highest frequency peak eliminates most of the ECG's morphological disturbance on the non-shockable rhythms. However, the shockable rhythms usually have dynamics in the frequency range of (3-6) Hz, which in certain cases coincide with CPR compression's harmonic frequencies, hence, removing them may lead to destruction of the shockable signal's dynamics. The proposed algorithm achieves CPR artifact removal without compromising the integrity of the shockable rhythm by considering three different spectral factors. The dataset from the PhysioNet archive was used to develop this condition-based approach. To quantify the performance of the approach on a separate dataset, three performance metrics were computed: the correlation coefficient, signal-to-noise ratio (SNR), and accuracy of Defibtech's shock decision algorithm. This dataset, containing 14 s ECG segments of different types of rhythms from 458 subjects, belongs to Defibtech commercial AED's validation set. The CPR artifact data from 52 different resuscitators were added to artifact-free ECG data to create 23,816 CPR-contaminated data segments. From this, 82% of the filtered shockable and 70% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; this value was only 13 and 12% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. The SNR improvement was 4.5 ± 2.5 dB, averaging over the entire dataset. Defibtech's rhythm analysis algorithm was applied to the filtered data. We found a sensitivity improvement from 67.7 to 91.3% and 62.7 to 78% for VF and rapid VT, respectively, and specificity improved from 96.2 to 96.5% and 91.5 to 92.7% for normal sinus rhythm (NSR) and other non-shockables, respectively.

Machine learning and feature engineering for predicting pulse presence during chest compressions

Current resuscitation protocols require pausing chest compressions during cardiopulmonary resuscitation (CPR) to check for a pulse. However, pausing CPR when a patient is pulseless can worsen patient outcomes. Our objective was to design and evaluate an ECG-based algorithm that predicts pulse presence with or without CPR. We evaluated 383 patients being treated for out-of-hospital cardiac arrest with real-time ECG, impedance and audio recordings. Paired ECG segments having an organized rhythm immediately preceding a pulse check (during CPR) and during the pulse check (without CPR) were extracted. Patients were randomly divided into 60% training and 40% test groups. From training data, we developed an algorithm to predict the clinical pulse presence based on the wavelet transform of the bandpass-filtered ECG. Principal component analysis was used to reduce dimensionality, and we then trained a linear discriminant model using three principal component modes as input features. Overall, 38% (351/912) of checks had a spontaneous pulse. AUCs for predicting pulse presence with and without CPR on test data were 0.84 (95% CI (0.80, 0.88)) and 0.89 (95% CI (0.86, 0.92)), respectively. This ECG-based algorithm demonstrates potential to improve resuscitation by predicting the presence of a spontaneous pulse without pausing CPR with moderate accuracy.

Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation

High performance of the shock advisory analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important for better management of the resuscitation protocol. It should provide fewer interruptions of chest compressions (CC) for non-shockable organized rhythms (OR) and Asystole, or prompt CC stopping for early treatment of shockable ventricular fibrillation (VF). Major disturbing factors are strong CC artifacts corrupting raw ECG, which we aimed to analyze with optimized end-to-end convolutional neural network (CNN) without pre-filtering or additional sensors. The hyperparameter random search of 1500 CNN models with 2-7 convolutional layers, 5-50 filters and 5-100 kernel sizes was done on large databases from independent OHCA interventions for training (3001 samples) and validation (2528 samples). The best model, named CNN3-CC-ECG network with three convolutional layers (filters@kernels: 5@5,25@20,50@20) presented Sensitivity Se(VF) = 89%(268/301), Specificity Sp(OR) = 91.7%(1504/1640), Sp(Asystole) = 91.1%(3325/3650) on an independent test OHCA database. CNN3-CC-ECG's ability to effectively extract features from raw ECG signals during CPR was comprehensively demonstrated, and the dependency on the CPR corruption level in ECG was tested. We denoted a significant drop of Se(VF) = 74.2% and Sp(OR) = 84.6% in very strong CPR artifacts with a signal-to-noise ratio of SNR < -9 dB, p < 0.05. Otherwise, for strong, moderate and weak CC artifacts (SNR > -9 dB, -6 dB, -3 dB), we observed insignificant performance differences: Se(VF) = 92.5-96.3%, Sp(OR) = 93.4-95.5%, Sp(Asystole) = 92.6-94.0%, p > 0.05. Performance stability with respect to CC rate was validated. Generalizable application of the optimized computationally efficient CNN model was justified by an independent OHCA database, which to our knowledge is the largest test dataset with real-life cardiac arrest rhythms during CPR.

Optimizing defibrillation during cardiac arrest

PURPOSE OF REVIEW

Current cardiac arrest guidelines are based on a fixed, time-based defibrillation strategy. Rhythm analysis and shock delivery (if indicated) are repeated every 2 min requiring cyclical interruptions of chest compressions. This approach has several downsides, such as the need to temporarily stop cardiopulmonary resuscitation (CPR) for a variable amount of time, thus reducing myocardial perfusion and decreasing the chance of successful defibrillation. A tailored defibrillation strategy should identify treatment priority for each patient, that is chest compressions (CCS) or defibrillation, minimize CCs interruptions, speed up the delivery of early effective defibrillation and reduce the number of ineffective shocks.

RECENT FINDINGS

Real-time ECG analysis (using adaptive filters, new algorithms robust to chest compressions artifacts and shock-advisory algorithms) is an effective strategy to correctly identify heart rhythm during CPR and reduce the hands-off time preceding a shock. Similarly, ventricular fibrillation waveform analysis, that is amplitude spectrum area (AMSA) represents a well established approach to reserve defibrillation in patients with high chance of shock success and postpone it when ventricular fibrillation termination is unlikely. Both approaches demonstrated valuable results in improving cardiac arrest outcomes in experimental and observational study.

SUMMARY

Real-time ECG analysis and AMSA have the potential to predict ventricular fibrillation termination, return of spontaneous circulation and even survival, with discretely high confidence. Prospective studies are now necessary to validate these new approaches in the clinical scenario.

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

Background

Amplitude spectrum area (AMSA) calculated from ventricular fibrillation (VF) can be used to monitor the effectiveness of chest compression (CC) and optimize the timing of defibrillation. However, reliable AMSA can only be obtained during CC pause because of artifacts. In this study, we sought to develop a method for estimating AMSA during cardiopulmonary resuscitation (CPR) using only the electrocardiogram (ECG) waveform.

Methods

Intervals of 8 seconds ECG and CC-related references, including 4 seconds during CC and an adjacent 4 seconds without CC, were collected before 1,008 defibrillation shocks from 512 out-of-hospital cardiac arrest patients. Signal quality was analyzed based on the irregularity of autocorrelation of VF. If signal quality index (SQI) was high, AMSA would be calculated from the original signal. Otherwise, CC-related artifacts would be constructed and suppressed using the least mean square filter from VF before calculation of AMSA. The algorithm was optimized using 480 training shocks and evaluated using 528 independent testing shocks.

Results

Overall, CC resulted in lower SQI [0.15 (0.04-0.61) with CC vs. 0.75 (0.61-0.83) without CC, P<0.01] and higher AMSA [11.2 (7.7-16.2) with CC vs. 7.2 (4.9-10.6) mVHz without CC, P<0.01] values. The predictive accuracy (49.2% vs. 66.5%, P<0.01) and area under the receiver operating characteristic curve (AUC) (0.647 vs. 0.734, P<0.01) were significantly decreased during CC. Using the proposed method, the estimated AMSA was 7.1 (5.0-15.2) mVHz, the predictive accuracy was 67.0% and the AUC was 0.713, which were all comparable with those calculated without CC.

Conclusions

Using the signal quality-based artifact suppression method, AMSA can be reliably estimated and continuously monitored during CPR.

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation

Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimizing interruptions in CPR increases the chance of survival. Methods and Results The objective of this study was to apply a deep-learning algorithm using convolutional layers, residual networks, and bidirectional long short-term memory method to classify shockable versus nonshockable rhythms in the presence and absence of CPR artifact. Forty subjects' data from Physionet with 1131 shockable and 2741 nonshockable samples contaminated with 43 different CPR artifacts that were acquired from a commercial automated external defibrillator during asystole were used. We had separate data as train and test sets. Using our deep neural network model, the sensitivity and specificity of the shock versus no-shock decision for the entire data set over the 4-fold cross-validation sets were 95.21% and 86.03%, respectively. This result was based on the training and testing of the model using ECG data in both the presence and the absence of CPR artifact. For ECG without CPR artifact, the sensitivity was 99.04% and the specificity was 95.2%. A sensitivity of 94.21% and a specificity of 86.14% were obtained for ECG with CPR artifact. In addition to 4-fold cross-validation sets, we also examined leave-one-subject-out validation. The sensitivity and specificity for the case of leave-one-subject-out validation were 92.71% and 97.6%, respectively. Conclusions The proposed trained model can make shock versus nonshock decision in automated external defibrillators, regardless of CPR status. The results meet the American Heart Association's sensitivity requirement (>90%).

A method to predict ventricular fibrillation shock outcome during chest compressions

BACKGROUND

Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes and could potentially enable a patient-specific, guided approach to resuscitation. However, VF analysis during resuscitation is confounded by cardiopulmonary resuscitation (CPR) artifact in the ECG, challenging continuous application to guide therapy throughout resuscitation. We therefore sought to design a method to predict VF shock outcomes during CPR.

METHODS

Study data included 4577 5-s VF segments collected during and without CPR prior to defibrillation attempts in N = 1151 arrest patients. Using training data (460 patients), an algorithm was designed to predict the VF shock outcomes of defibrillation success (return of organized ventricular rhythm) and functional survival (Cerebral Performance Category 1-2). The algorithm was designed with variable-frequency notch filters to reduce CPR artifact in the ECG based on real-time chest compression rate. Ten ECG features and three dichotomous patient characteristics were developed to predict outcomes. These variables were combined using support vector machines and logistic regression. Algorithm performance was evaluated by area under the receiver operating characteristic curve (AUC) to predict outcomes in validation data (691 patients).

RESULTS

AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71-0.77) during CPR and 0.77 (0.74-0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72-0.78) during CPR and 0.76 (0.74-0.79) without CPR.

CONCLUSION

A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy.

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks

Chest compressions during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may provoque inaccurate rhythm classification by the algorithm of the defibrillator. The objective of this study was to design an algorithm to produce reliable shock/no-shock decisions during CPR using convolutional neural networks (CNN). A total of 3319 ECG segments of 9 s extracted during chest compressions were used, whereof 586 were shockable and 2733 nonshockable. Chest compression artifacts were removed using a Recursive Least Squares (RLS) filter, and the filtered ECG was fed to a CNN classifier with three convolutional blocks and two fully connected layers for the shock/no-shock classification. A 5-fold cross validation architecture was adopted to train/test the algorithm, and the proccess was repeated 100 times to statistically characterize the performance. The proposed architecture was compared to the most accurate algorithms that include handcrafted ECG features and a random forest classifier (baseline model). The median (90% confidence interval) sensitivity, specificity, accuracy and balanced accuracy of the method were 95.8% (94.6-96.8), 96.1% (95.8-96.5), 96.1% (95.7-96.4) and 96.0% (95.5-96.5), respectively. The proposed algorithm outperformed the baseline model by 0.6-points in accuracy. This new approach shows the potential of deep learning methods to provide reliable diagnosis of the cardiac rhythm without interrupting chest compression therapy.

Anticipatory manual defibrillator charging during advanced life support: A scoping review

Background

Some resuscitation services advocate or teach routine manual defibrillator charging prior to a rhythm check during cardiopulmonary resuscitation.

Objectives

We aimed to review the evidence for anticipatory defibrillator charging compared with charging after a shockable rhythm is confirmed.

Methods

This scoping review was performed according to a specific methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Grey literature was also reviewed using similar methodology and included in the results.

Results

There are no randomized clinical trials studying anticipatory manual defibrillator charging. The limited available data does not address critical or important patient outcomes such as defibrillation success, return of spontaneous circulation, survival to hospital discharge or neurological outcomes. Evidence primarily from manikin studies and the grey literature suggests that anticipatory charging is feasible, safe, and can reduce the total pause duration during the period of chest compression between rhythm checks, but can increase the pre-shock pause and total peri-shock pause duration.

Conclusions

Anticipatory manual defibrillator charging appears to be feasible in the clinical setting, although its impact on clinical outcomes is uncertain. Future studies of anticipatory charging should focus on clinical outcomes.

A Method to Detect Presence of Chest Compressions During Resuscitation Using Transthoracic Impedance

OBJECTIVE

Interruptions in chest compressions during treatment of out-of-hospital cardiac arrest are associated with lower likelihood of successful resuscitation. Real-time automated detection of chest compressions may improve CPR administration during resuscitation, and could facilitate application of next-generation ECG algorithms that employ different parameters depending on compression state. In contrast to accelerometer sensors, transthoracic impedance (TTI) is commonly acquired by defibrillators. We sought to develop and evaluate the performance of a TTI-based algorithm to automatically detect chest compressions.

METHODS

Five-second TTI segments were collected from patients with out-of-hospital cardiac arrest treated by one of four defibrillator models. Segments with and without chest compressions were collected prior to each of the first four defibrillation shocks (when available) from each case. Patients were divided randomly into 40% training and 60% validation groups. From the training segments, we identified spectral and time-domain features of the TTI associated with compressions. We used logistic regression to predict compression state from these features. Performance was measured by sensitivity and specificity in the validation set. The relationship between performance and TTI segment length was also evaluated.

RESULTS

The algorithm was trained using 1859 segments from 460 training patients. Validation sensitivity and specificity were >98% using 2727 segments from 691 validation patients. Validation performance was significantly reduced using segments shorter than 3.2 s.

CONCLUSIONS

A novel method can reliably detect the presence of chest compressions using TTI. These results suggest potential to provide real-time feedback in order to improve CPR performance or facilitate next-generation ECG rhythm algorithms during resuscitation.

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.

Enhancement of capnogram waveform in the presence of chest compression artefact during cardiopulmonary resuscitation

BACKGROUND

Current resuscitation guidelines emphasize the use of waveform capnography to help guide rescuers during cardiopulmonary resuscitation (CPR). However, chest compressions often cause oscillations in the capnogram, impeding its reliable interpretation, either visual or automated. The aim of the study was to design an algorithm to enhance waveform capnography by suppressing the chest compression artefact.

METHODS

Monitor-defibrillator recordings from 202 patients in out-of-hospital cardiac arrest were analysed. Capnograms were classified according to the morphology of the artefact. Ventilations were annotated using the transthoracic impedance signal acquired through defibrillation pads. The suppression algorithm is designed to operate in real-time, locating distorted intervals and restoring the envelope of the capnogram. We evaluated the improvement in automated ventilation detection, estimation of ventilation rate, and detection of excessive ventilation rates (over-ventilation) using the capnograms before and after artefact suppression.

RESULTS

A total of 44 267 ventilations were annotated. After artefact suppression, sensitivity (Se) and positive predictive value (PPV) of the ventilation detector increased from 91.9/89.5% to 98.0/97.3% in the distorted episodes (83/202). Improvement was most noticeable for high-amplitude artefact, for which Se/PPV raised from 77.6/73.5% to 97.1/96.1%. Estimation of ventilation rate and detection of over-ventilation also upgraded. The suppression algorithm had minimal impact in non-distorted data.

CONCLUSION

Ventilation detection based on waveform capnography improved after chest compression artefact suppression. Moreover, the algorithm enhances the capnogram tracing, potentially improving its clinical interpretation during CPR. Prospective research in clinical settings is needed to understand the feasibility and utility of the method.

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.

Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform

BACKGROUND

During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the feasibility of several filtering techniques for suppressing the artifact to improve the accuracy of ventilation detection.

MATERIALS AND METHODS

We gathered a database of 232 out-of-hospital cardiac arrest defibrillator recordings containing concurrent capnograms, compression depth and transthoracic impedance signals. Capnograms were classified as non-distorted or distorted by chest compression artifact. All chest compression and ventilation instances were also annotated. Three filtering techniques were explored: a fixed-coefficient (FC) filter, an open-loop (OL) adaptive filter, and a closed-loop (CL) adaptive filter. The improvement in ventilation detection was assessed by comparing the performance of a capnogram-based ventilation detection algorithm with original and filtered capnograms.

RESULTS

Sensitivity and positive predictive value of the ventilation algorithm improved from 91.9%/89.5% to 97.7%/96.5% (FC filter), 97.6%/96.7% (OL), and 97.0%/97.1% (CL) for the distorted capnograms (42% of the whole set). The highest improvement was obtained for the artifact named type III, for which performance improved from 77.8%/74.5% to values above 95.5%/94.5%. In addition, errors in the measurement of ventilation rate decreased and accuracy in the detection of over-ventilation increased with filtered capnograms.

CONCLUSIONS

Capnogram-based ventilation detection during CPR was enhanced after suppressing the artifact caused by chest compressions. All filtering approaches performed similarly, so the simplicity of fixed-coefficient filters would take advantage for a practical implementation.

Manual Versus Semiautomatic Rhythm Analysis and Defibrillation for Out-of-Hospital Cardiac Arrest

BACKGROUND

Although manual and semiautomatic external defibrillation (SAED) are commonly used in the management of out-of-hospital cardiac arrest, the optimal strategy is not known. We hypothesized that SAED would reduce the time to first shock and lead to higher rates of cardioversion and survival compared with a manual strategy.

METHODS AND RESULTS

Between July 2005 and June 2015, we included adult out-of-hospital cardiac arrest of presumed cardiac pathogenesis. On October 2012, a treatment protocol using SAED was introduced after years of manual defibrillation. The effect of the SAED implementation on the time to first shock, successful cardioversion, and patient outcomes was assessed using interrupted time series regression adjusting for arrest factors and temporal trend. Of the 14 776 cases, 10 224 (69.2%) and 4552 (30.8%) occurred during the manual and SAED protocols, respectively. Although the proportion of patients shocked within 2 minutes of arrival increased during the SAED protocol for initial shockable rhythms (from 58.9% to 69.2%; P<0.001), there was no difference in unadjusted rate of successful cardioversion after first shock (from 12.3% to 13.8%; P=0.13). After adjustment, the odds of delivering the first shock within 2 minutes of arrival increased under the SAED protocol (adjusted odds ratio [AOR], 1.72; 95% confidence interval [CI], 1.32-2.26; P<0.001). Despite this, the SAED protocol was associated with a reduction in survival to hospital discharge (AOR, 0.71; 95% CI, 0.55-0.92; P=0.009), event survival (AOR, 0.74; 95% CI, 0.62-0.88; P=0.001), and prehospital return of spontaneous circulation (AOR, 0.81; 95% CI, 0.68-0.96; P=0.01) when compared with the manual protocol. There was also no improvement in the rate of successful cardioversion after first shock (AOR, 0.73; 95% CI, 0.51-1.06; P=0.10).

CONCLUSIONS

Although SAED improved the time to first shock, this did not translate into higher rates of successful cardioversion or survival after out-of-hospital cardiac arrest. Advanced life support providers should be trained to use a manual defibrillation protocol.

An automatic system for the comprehensive retrospective analysis of cardiac rhythms in resuscitation episodes

AIM

An automatic resuscitation rhythm annotator (ARA) would facilitate and enhance retrospective analysis of resuscitation data, contributing to a better understanding of the interplay between therapy and patient response. The objective of this study was to define, implement, and demonstrate an ARA architecture for complete resuscitation episodes, including chest compression pauses (CC-pauses) and chest compression intervals (CC-intervals).

METHODS

We analyzed 126.5h of ECG and accelerometer-based chest-compression depth data from 281 out-of-hospital cardiac arrest (OHCA) patients. Data were annotated by expert reviewers into asystole (AS), pulseless electrical activity (PEA), pulse-generating rhythm (PR), ventricular fibrillation (VF), and ventricular tachycardia (VT). Clinical pulse annotations were based on patient-charts and impedance measurements. An ARA was developed for CC-pauses, and was used in combination with a chest compression artefact removal filter during CC-intervals. The performance of the ARA was assessed in terms of the unweighted mean of sensitivities (UMS).

RESULTS

The UMS of the ARA were 75.0% during CC-pauses and 52.5% during CC-intervals, 55-points and 32.5-points over a random guess (20% for five categories). Filtering increased the UMS during CC-intervals by 5.2-points. Sensitivities for AS, PEA, PR, VF, and VT were 66.8%, 55.8%, 86.5%, 82.1% and 83.8% during CC-pauses; and 51.1%, 34.1%, 58.7%, 86.4%, and 32.1% during CC-intervals.

CONCLUSIONS

A general ARA architecture was defined and demonstrated on a comprehensive OHCA dataset. Results showed that semi-automatic resuscitation rhythm annotation, which may involve further revision/correction by clinicians for quality assurance, is feasible. The performance (UMS) dropped significantly during CC-intervals and sensitivity was lowest for PEA.

ECG-Based Classification of Resuscitation Cardiac Rhythms for Retrospective Data Analysis

OBJECTIVE

There is a need to monitor the heart rhythm in resuscitation to improve treatment quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse-generating rhythm (PR). Manual annotation of rhythms is time-consuming and infeasible for large datasets. Our objective was to develop ECG-based algorithms for the retrospective and automatic classification of resuscitation cardiac rhythms.

METHODS

The dataset consisted of 1631 3-s ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. In total, 47 wavelet- and time-domain-based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied.

RESULTS

The best results were obtained for ANN classifier with Bayesian regularization backpropagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively.

CONCLUSIONS

The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low.

SIGNIFICANCE

We have made an important step toward making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.

Short ECG segments predict defibrillation outcome using quantitative waveform measures

AIM

Quantitative waveform measures of the ventricular fibrillation (VF) electrocardiogram (ECG) predict defibrillation outcome. Calculation requires an ECG epoch without chest compression artifact. However, pauses in CPR can adversely affect survival. Thus the potential use of waveform measures is limited by the need to pause CPR. We sought to characterize the relationship between the length of the CPR-free epoch and the ability to predict outcome.

METHODS

We conducted a retrospective investigation using the CPR-free ECG prior to first shock among out-of-hospital VF cardiac arrest patients in a large metropolitan region (n=442). Amplitude Spectrum Area (AMSA) and Median Slope (MS) were calculated using ECG epochs ranging from 5s to 0.2s. The relative ability of the measures to predict return of organized rhythm (ROR) and neurologically-intact survival was evaluated at different epoch lengths by calculating the area under the receiver operating characteristic curve (AUC) using the 5-s epoch as the referent group.

RESULTS

Compared to the 5-s epoch, AMSA performance declined significantly only after reducing epoch length to 0.2s for ROR (AUC 0.77-0.74, p=0.03) and with epochs of ≤0.6s for neurologically-intact survival (AUC 0.72-0.70, p=0.04). MS performance declined significantly with epochs of ≤0.8s for ROR (AUC 0.78-0.77, p=0.04) and with epochs ≤1.6s for neurologically-intact survival (AUC 0.72-0.71, p=0.04).

CONCLUSION

Waveform measures predict defibrillation outcome using very brief ECG epochs, a quality that may enable their use in current resuscitation algorithms designed to limit CPR interruption.

An Enhanced Adaptive Filtering Method for Suppressing Cardiopulmonary Resuscitation Artifact

Cardiopulmonary resuscitation (CPR) must be interrupted for reliable rhythm analysis in current automatic external defibrillators because of artifacts produced by chest compressions. However, interruptions in CPR adversely affect the restoration of spontaneous circulation and survival. Suppressing CPR artifacts by digital signal processing techniques is a promising method to enable rhythm analysis during chest compressions, which would eliminate CPR interruptions for rhythm analysis. Although numerous methods have been developed to suppress CPR artifacts, the accuracy of rhythm analysis is still inadequate due to the residual artifact components in the filtered signal. This study proposes an enhanced adaptive filtering method to suppress CPR artifacts. A total of 183 shockable and 453 nonshockable segments of ECG signal, together with CPR-related reference signal, were extracted from 233 out of hospital cardiac arrest patients. The method was optimized on a training set with 85 shockable and 211 nonshockable segments, and evaluated on a testing set with 98 shockable and 242 nonshockable segments. Compared with artifact corrupted ECG signals, the signal-to-noise ratio (SNR) increased from -9.8 ± 12.5 to 11.2 ± 11.8 dB, and the accuracy was improved from 74.1% to 92.0% after filtering with the proposed method. Compared with the traditional adaptive filter, the SNR was improved by 1.7 dB and the accuracy was improved by 5.6 points. These results indicated that the proposed method could effectively suppress the chest compression related artifacts and improve the accuracy of rhythm analysis during uninterrupted CPR.

Hands-on defibrillation and electrocardiogram artefact filtering technology increases chest compression fraction and decreases peri-shock pause duration in a simulation model of cardiac arrest

Background

Reducing pauses during cardiopulmonary resuscitation (CPR) compressions result in better outcomes in cardiac arrest. Artefact filtering technology (AFT) gives rescuers the opportunity to visualize the underlying electrocardiogram (ECG) rhythm during chest compressions, and reduces the pauses that occur before and after delivering a shock. We conducted a simulation study to measure the reduction of peri-shock pause and impact on chest compression fraction (CCF) through AFT.

Methods

In a simulator setting, participants were given a standardized cardiac arrest scenario and were randomly assigned to perform CPR/defibrillation using the protocol from one of three experimental arms: 1) Standard of Care (pauses for rhythm analysis and shock delivery); 2) AFT (no pauses for rhythm analysis, but a pause for defibrillation); or 3) AFT with hands-on defibrillation (no pauses for rhythm analysis or defibrillation). The primary outcomes were CCF and peri-shock pause duration, with secondary outcomes of pre- and post-shock pause duration.

Results

AFT with hands-on defibrillation was found to have the highest CCF (86.4%), as compared to AFT alone (83.8%, p<0.001), and both groups significantly improved CCF in comparison with the Standard of Care (76.7%, p<0.001). AFT with hands-on defibrillation was associated with a reduced peri-shock pause (2.6 seconds) as compared to AFT alone (5.3 seconds, p<0.001), and the Standard of Care (7.4 seconds, p<0.001).

Conclusions

In this cardiac arrest model, AFT results in a greater CCF by reducing peri-shock pause duration. There is also a small but detectable improvement in CCF with the addition of hands-on defibrillation.

Fully automatic rhythm analysis during chest compression pauses

AIM

Chest compression artefacts impede a reliable rhythm analysis during cardiopulmonary resuscitation (CPR). These artefacts are not present during ventilations in 30:2 CPR. The aim of this study is to prove that a fully automatic method for rhythm analysis during ventilation pauses in 30:2 CPR is reliable an accurate.

METHODS

For this study 1414min of 30:2 CPR from 135 out-of-hospital cardiac arrest cases were analysed. The data contained 1942 pauses in compressions longer than 3.5s. An automatic pause detector identified the pauses using the transthoracic impedance, and a shock advice algorithm (SAA) diagnosed the rhythm during the detected pauses. The SAA analysed 3-s of the ECG during each pause for an accurate shock/no-shock decision.

RESULTS

The sensitivity and PPV of the pause detector were 93.5% and 97.3%, respectively. The sensitivity and specificity of the SAA in the detected pauses were 93.8% (90% low CI, 90.0%) and 95.9% (90% low CI, 94.7%), respectively. Using the method, shocks would have been advanced in 97% of occasions. For patients in nonshockable rhythms, rhythm reassessment pauses would be avoided in 95.2% (95% CI, 91.6-98.8) of occasions, thus increasing the overall chest compression fraction (CCF).

CONCLUSION

An automatic method could be used to safely analyse the rhythm during ventilation pauses. This would contribute to an early detection of refibrillation, and to increase CCF in patients with nonshockable rhythms.

A reliable method for rhythm analysis during cardiopulmonary resuscitation

Interruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.