Suppression of the cardiopulmonary resuscitation artefacts using the instantaneous chest compression rate extracted from the thoracic impedance

Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: In-hospital setting

AIM OF THE STUDY

This study aimed to develop an artificial intelligence (AI) model capable of predicting shockable rhythms from electrocardiograms (ECGs) with compression artifacts using real-world data from emergency department (ED) settings. Additionally, we aimed to explore the black box nature of AI models, providing explainability.

METHODS

This study is retrospective, observational study using a prospectively collected database. Adult patients who presented to the ED with cardiac arrest or experienced cardiac arrest in the ED between September 2021 and February 2024 were included. ECGs with a compression artifact of 5 s before every rhythm check were used for analysis. The AI model was designed based on convolutional neural networks. The ECG data were assigned into training, validation, and testing sets on a per-patient basis to ensure that ECGs from the same patient did not appear in multiple sets. Gradient-weighted class activation mapping was employed to demonstrate AI explainability.

RESULTS

A total of 1,889 ECGs with compression artifacts from 172 patients were used. The area under the receiver operating characteristic curve (AUROC) for shockable rhythm prediction was 0.8672 (95% confidence interval [CI]: 0.8161-0.9122). The AUROCs for manual and mechanical compression were 0.8771 (95% CI: 0.8054-0.9408) and 0.8466 (95% CI: 0.7630-0.9138), respectively.

CONCLUSION

This study was the first to accurately predict shockable rhythms during compression using an AI model trained with actual patient ECGs recorded during resuscitation. Furthermore, we demonstrated the explainability of the AI. This model can minimize interruption of cardiopulmonary resuscitation and potentially lead to improved outcomes.

Extracting physiologic and clinical data from defibrillators for research purposes to improve treatment for patients in cardiac arrest

Background

A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO2, which allows for research on how patients demonstrate different responses to CPR. The aim of this review is to give an overview of methodological challenges and opportunities in using defibrillator data for research.

Methods

The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen's linear model, Weibull regression and joint models.

Conclusions

The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.

Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy

This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 CPR episodes from out-of-hospital cardiac arrest (OHCA) interventions reviewed in a period of interest from 30 s before to 10 s after regular analysis of automated external defibrillators (AEDs). Three convolutional neural networks (CNNs) with raw ECG input (duration of 5, 10, and 15 s) were applied for the shock advisory decision during CPR in 26 sequential analyses shifted by 1 s. The start and stop of chest compressions (CC) can occur at arbitrary times in sequential slides; therefore, the sliding hands-off time (sHOT) quantifies the cumulative CC-free portion of the analyzed ECG. An independent test with CPR episodes in 393 ventricular fibrillations (VF), 177 normal sinus rhythms (NSR), 1848 other non-shockable rhythms (ONR), and 3979 asystoles (ASYS) showed a substantial improvement of VF sensitivity when increasing the analysis duration from 5 s to 10 s. Specificity was not dependent on the ECG analysis duration. The 10 s CNN model presented the best performance: 92-94.4% (VF), 92.2-94% (ASYS), 96-97% (ONR), and 98.2-99.5% (NSR) for sliding decision times during CPR; 98-99% (VF), 98.2-99.8% (ASYS), 98.8-99.1 (ONR), and 100% (NSR) for sliding decision times after end of CPR. We identified the importance of sHOT as a reliable predictor of performance, accounting for the minimal sHOT interval of 2-3 s that provides a reliable rhythm detection satisfying the American Heart Association (AHA) standards for AED rhythm analysis. The presented technology for sliding shock advisory decision during CPR achieved substantial performance improvement in short hands-off periods (>2 s), such as insufflations or pre-shock pauses. The performance was competitive despite 1-2.8% point lower ASYS detection during CPR than the standard requirement (95%) for non-noisy ECG signals. The presented deep learning strategy is a basis for improved CPR practices involving both continuous CC and CC with insufflations, associated with minimal CC interruptions for reconfirmation of non-shockable rhythms (minimum hands-off time) and early treatment of VF (minimal pre-shock pauses).

Methods for calculating ventilation rates during resuscitation from out-of-hospital cardiac arrest

OBJECTIVE

Ventilation control is important during resuscitation from out-of-hospital cardiac arrest (OHCA). We compared different methods for calculating ventilation rates (VR) during OHCA.

METHODS

We analyzed data from the Pragmatic Airway Resuscitation Trial, identifying ventilations through capnogram recordings. We determined VR by: 1) counting the number of breaths within a time epoch ("counted" VR), and 2) calculating the mean of the inverse of measured time between breaths within a time epoch ("measured" VR). We repeated the VR estimates using different time epochs (10, 20, 30, 60 sec). We defined hypo- and hyperventilation as VR <6 and >12 breaths/min, respectively. We assessed differences in estimated hypo- and hyperventilation with each VR measurement technique.

RESULTS

Of 3,004 patients, data were available for 1,010. With the counted method, total hypoventilation increased with longer time epochs ([10-s epoch: 75 sec hypoventilation] to [60-s epoch: 97 sec hypoventilation]). However, with the measured method, total hypoventilation decreased with longer time epochs ([10-s epoch: 223 sec hypoventilation] to [60-s epoch: 150 sec hypoventilation]). With the counted method, the total duration of hyperventilation decreased with longer time epochs ([10-s epochs: 35 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). With the measured method, total hyperventilation decreased with longer time epochs ([10-s epoch: 78 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). Differences between the measured and counted estimates were smallest with a 60-s time epoch.

CONCLUSIONS

Quantifications of hypo- and hyperventilation vary with the applied measurement methods. Measurement methods are important when characterizing ventilation rates in OHCA.

Airway Strategy and Ventilation Rates in the Pragmatic Airway Resuscitation Trial

BACKGROUND

We sought to describe ventilation rates during out-of-hospital cardiac arrest (OHCA) resuscitation and their associations with airway management strategy and outcomes.

METHODS

We analyzed continuous end-tidal carbon dioxide capnography data from adult OHCA enrolled in the Pragmatic Airway Resuscitation Trial (PART). Using automated signal processing techniques, we determined continuous ventilation rate for consecutive 10-second epochs after airway insertion. We defined hypoventilation as a ventilation rate <6 breaths/min. We defined hyperventilation as a ventilation rate >12 breaths/min. We compared differences in total and percentage post-airway hyper- and hypoventilation between airway interventions (laryngeal tube (LT) vs. endotracheal intubation (ETI). We also determined associations between hypo-/hyperventilation and OHCA outcomes (ROSC, 72-hour survival, hospital survival, hospital survival with favorable neurologic status).

RESULTS

Adequate post-airway capnography were available for 1,010 (LT n=714, ETI n=296) of 3,004 patients. Median ventilation rates were: LT 8.0 (IQR 6.5-9.6) breaths/min, ETI 7.9 (6.5-9.7) breaths/min. Total duration and percentage of post-airway time with hypoventilation were similar between LT and ETI: median 1.8 vs. 1.7 minutes, p=0.94; median 10.5% vs. 11.5%, p=0.60. Total duration and percentage of post-airway time with hyperventilation were similar between LT and ETI: median 0.4 vs. 0.4 minutes, p=0.91; median 2.1% vs. 1.9%, p=0.99. Hypo- and hyperventilation exhibited limited associations with OHCA outcomes.

CONCLUSION

In the PART Trial, EMS personnel delivered post-airway ventilations at rates satisfying international guidelines, with only limited hypo- or hyperventilation. Hypo- and hyperventilation durations did not differ between airway management strategy and exhibited uncertain associations with OCHA outcomes.

Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services.

AIM

To introduce and evaluate a methodology to automatically extract cardiopulmonary resuscitation (CPR) quality data in a uniform and systematic way from OHCA datasets from multiple heterogeneous sources.

METHODS

A dataset of 2236 OHCA cases from multiple defibrillator models and manufacturers was analyzed. Chest compressions were automatically identified using the thoracic impedance and compression depth signals. Device event time-stamps and clinical annotations were used to set the start and end of the analysis interval, and to identify periods with spontaneous circulation. A manual audit of the automatic annotations was conducted and used as gold standard. Chest compression fraction (CCF), rate (CCR) and interruption ratio were computed as CPR quality variables. The unsigned error between the automated procedure and the gold standard was calculated.

RESULTS

Full-episode median errors below 2% in CCF, 1 min^-1 in CCR, and 1.5% in interruption ratio, were measured for all signals and devices. The proportion of cases with large errors (>10% in CCF and interruption ratio, and >10 min^-1 in CCR) was below 10%. Errors were lower for shorter sub-intervals of interest, like the airway insertion interval.

CONCLUSIONS

An automated methodology was validated to accurately compute CPR metrics in large and heterogeneous OHCA datasets. Automated processing of defibrillator files and the associated clinical annotations enables the aggregation and analysis of CPR data from multiple sources.

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.

Shock decision algorithm for use during load distributing band cardiopulmonary resuscitation

AIM

Chest compressions delivered by a load distributing band (LDB) induce artefacts in the electrocardiogram. These artefacts alter shock decisions in defibrillators. The aim of this study was to demonstrate the first reliable shock decision algorithm during LDB compressions.

METHODS

The study dataset comprised 5813 electrocardiogram segments from 896 cardiac arrest patients during LDB compressions. Electrocardiogram segments were annotated by consensus as shockable (1154, 303 patients) or nonshockable (4659, 841 patients). Segments during asystole were used to characterize the LDB artefact and to compare its characteristics to those of manual artefacts from other datasets. LDB artefacts were removed using adaptive filters. A machine learning algorithm was designed for the shock decision after filtering, and its performance was compared to that of a commercial defibrillator's algorithm.

RESULTS

Median (90% confidence interval) compression frequencies were lower and more stable for the LDB than for the manual artefact, 80 min^-1 (79.9-82.9) vs. 104.4 min^-1 (48.5-114.0). The amplitude and waveform regularity (Pearson's correlation coefficient) were larger for the LDB artefact, with 5.5 mV (0.8-23.4) vs. 0.5 mV (0.1-2.2) (p < 0.001) and 0.99 (0.78-1.0) vs. 0.88 (0.55-0.98) (p < 0.001). The shock decision accuracy was significantly higher for the machine learning algorithm than for the defibrillator algorithm, with sensitivity/specificity pairs of 92.1/96.8% (machine learning) vs. 91.4/87.1% (defibrillator) (p < 0.001).

CONCLUSION

Compared to other cardiopulmonary resuscitation artefacts, removing the LDB artefact was challenging due to larger amplitudes and lower compression frequencies. The machine learning algorithm achieved clinically reliable shock decisions during LDB compressions.

Airway strategy and chest compression quality in the Pragmatic Airway Resuscitation Trial

BACKGROUND

Chest compression (CC) quality is associated with improved out-of-hospital cardiopulmonary arrest (OHCA) outcomes. Airway management efforts may adversely influence CC quality. We sought to compare the effects of initial laryngeal tube (LT) and initial endotracheal intubation (ETI) airway management strategies upon chest compression fraction (CCF), rate and interruptions in the Pragmatic Airway Resuscitation Trial (PART).

METHODS

We analyzed CPR process files collected from adult OHCA enrolled in PART. We used automated signal processing techniques and a graphical user interface to calculate CC quality measures and defined interruptions as pauses in chest compressions longer than 3 s. We determined CC fraction, rate and interruptions (number and total duration) for the entire resuscitation and compared differences between LT and ETI using t-tests. We repeated the analysis stratified by time before, during and after airway insertion as well as by successive 3-min time segments. We also compared CC quality between single vs. multiple airway insertion attempts, as well as between bag-valve-mask (BVM-only) vs. ETI or LT.

RESULTS

Of 3004 patients enrolled in PART, CPR process data were available for 1996 (1001 LT, 995 ETI). Mean CPR analysis duration were: LT 22.6 ± 10.8 min vs. ETI 25.3 ± 11.3 min (p < 0.001). Mean CC fraction (LT 88% vs. ETI 87%, p = 0.05) and rate (LT 114 vs. ETI 114 compressions per minute (cpm), p = 0.59) were similar between LT and ETI. Median number of CC interruptions were: LT 11 vs. ETI 12 (p = 0.001). Total CC interruption duration was lower for LT than ETI (LT 160 vs. ETI 181 s, p = 0.002); this difference was larger before airway insertion (LT 56 vs. ETI 78 s, p < 0.001). There were no differences in CC quality when stratified by 3-min time epochs.

CONCLUSION

In the PART trial, compared with ETI, LT was associated with shorter total CC interruption duration but not other CC quality measures. CC quality may be associated with OHCA airway management.

Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

Background

A transthoracic impedance (TTI) signal is an important indicator of the quality of chest compressions (CCs) during cardiopulmonary resuscitation (CPR). We proposed an automatic detection algorithm including the wavelet decomposition, fuzzy c-means (FCM) clustering, and deep belief network (DBN) to identify the compression and ventilation waveforms for evaluating the quality of CPR.

Methods

TTI signals were collected from a cardiac arrest model that electrically induced cardiac arrest in pigs. All signals were denoised using the wavelet and morphology method. The potential compression and ventilation waveforms were marked using an algorithm with a multi-resolution window. The compressions and ventilations in these waveforms were identified and classified using the FCM clustering and DBN methods.

Results

Using the FCM clustering method, the positive predictive values (PPVs) for compressions and ventilations were 99.7% and 95.7%, respectively. The sensitivities of recognition were 99.8% for compressions and 95.1% for ventilations. The DBN approach exhibited similar PPV and sensitivity results to the FCM clustering method. The time cost was satisfactory using either of these techniques.

Conclusions

Our findings suggest that FCM clustering and DBN can be utilized to effectively and accurately evaluate CPR quality, and provide information for improving the success rate of CPR. Our real-time algorithms using FCM clustering and DBN eliminated most distortions and noises effectively, and correctly identified the compression and ventilation waveforms with a low time cost.

Adult Basic Life Support: International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This 2020 International Consensus on Cardiopulmonary Resuscitation (CPR) and Emergency Cardiovascular Care Science With Treatment Recommendations on basic life support summarizes evidence evaluations performed for 20 topics that were prioritized by the Basic Life Support Task Force of the International Liaison Committee on Resuscitation. The evidence reviews include 16 systematic reviews, 3 scoping reviews, and 1 evidence update. Per agreement within the International Liaison Committee on Resuscitation, new or revised treatment recommendations were only made after a systematic review.

Systematic reviews were performed for the following topics: dispatch diagnosis of cardiac arrest, use of a firm surface for CPR, sequence for starting CPR (compressions-airway-breaths versus airway-breaths-compressions), CPR before calling for help, duration of CPR cycles, hand position during compressions, rhythm check timing, feedback for CPR quality, alternative techniques, public access automated external defibrillator programs, analysis of rhythm during chest compressions, CPR before defibrillation, removal of foreign-body airway obstruction, resuscitation care for suspected opioid-associated emergencies, drowning, and harm from CPR to victims not in cardiac arrest.

The topics that resulted in the most extensive task force discussions included CPR during transport, CPR before calling for help, resuscitation care for suspected opioid-associated emergencies, feedback for CPR quality, and analysis of rhythm during chest compressions. After discussion of the scoping reviews and the evidence update, the task force prioritized several topics for new systematic reviews.

Adult Basic Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This2020 International Consensus on Cardiopulmonary Resuscitation(CPR)and Emergency Cardiovascular Care Science With Treatment Recommendationson basic life support summarizes evidence evaluations performed for 22 topics that were prioritized by the Basic Life Support Task Force of the International Liaison Committee on Resuscitation. The evidence reviews include 16 systematic reviews, 5 scoping reviews, and 1 evidence update. Per agreement within the International Liaison Committee on Resuscitation, new or revised treatment recommendations were only made after a systematic review.

Systematic reviews were performed for the following topics: dispatch diagnosis of cardiac arrest, use of a firm surface for CPR, sequence for starting CPR (compressions-airway-breaths versus airway-breaths-compressions), CPR before calling for help, duration of CPR cycles, hand position during compressions, rhythm check timing, feedback for CPR quality, alternative techniques, public access automated external defibrillator programs, analysis of rhythm during chest compressions, CPR before defibrillation, removal of foreign-body airway obstruction, resuscitation care for suspected opioid-associated emergencies, drowning, and harm from CPR to victims not in cardiac arrest.

The topics that resulted in the most extensive task force discussions included CPR during transport, CPR before calling for help, resuscitation care for suspected opioid-associated emergencies, feedback for CPR quality, and analysis of rhythm during chest compressions. After discussion of the scoping reviews and the evidence update, the task force prioritized several topics for new systematic reviews.

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.

The performance of a new shock advisory algorithm to reduce interruptions during CPR

OBJECTIVE

To explore a new algorithm and strategy for rhythm analysis during chest compressions (CCs), and to improve the efficiency of cardiopulmonary resuscitation (CPR) by minimizing interruptions.

METHODS

The clinical data and ECG of patients with sudden cardiac arrest (CA) from three hospitals in China were collected with Philips MRx monitor/defibrillators. The length of each analyzed ECG segment was 23 s, the first 11.5 s was selected to contain CPR compressions, the next 5 s had no compressions, and the last 6.5 s had no requirement. Three experienced emergency doctors annotated the ECG segments without compression artifacts. A two-step analysis through CPR (ATC) algorithm was applied to the selected data. The first step was analysis during chest compressions. If a shockable rhythm was not detected, compression-free analysis followed. The results of the ATC algorithm were compared with the annotations by the physicians, to determine the sensitivity and specificity of the algorithm.

RESULTS

In total 166 CA patients were included with 100 out-of-hospital cardiac arrest (OHCA) patients and 66 in-hospital cardiac arrest (IHCA) patients. A total of 1578 ECG segments were analyzed, including 115 (7.3%) shockable rhythms, 1278 (81.0%) non-shockable rhythms, and 185 (11.7%) intermediate/unknown rhythms. The specificity of all non-shockable rhythms was 99.8% at the end of chest compressions, and 99.5% after analysis without compression artifact. 70.5% of ventricular fibrillation (VF) rhythms were detected by the end of chest compressions. After the CC-free analysis, 93.6% of VF was identified.

CONCLUSION

The ATC algorithm achieved sensitivity of 93.6% and specificity of 99.5% after the two-step analysis, and 70.5% of the patients with shockable rhythms did not require CC-free analysis. Such an approach has the potential to substantially reduce CC interruptions when identifying shockable rhythms.

Rhythm characteristics and patterns of change during cardiopulmonary resuscitation for in-hospital paediatric cardiac arrest

During paediatric cardiopulmonary resuscitation (CPR), patients may transition between pulseless electrical activity (PEA), asystole, ventricular fibrillation/tachycardia (VF/VT), and return of spontaneous circulation (ROSC). The aim of this study was to quantify the dynamic characteristics of this process.

METHODS

ECG recordings were collected in patients who received CPR at the Children's Hospital of Philadelphia (CHOP) between 2006 and 2013. Transitions between PEA (including bradycardia with poor perfusion), VF/VT, asystole, and ROSC were quantified by applying a multi-state statistical model with competing risks, and by smoothing the Nelson-Aalen estimator of cumulative hazard.

RESULTS

Seventy-four episodes of cardiac arrest were included. Median age of patients was 15 years [IQR 11-17], 50% were female and 62% had a respiratory aetiology of arrest. Presenting cardiac arrest rhythms were PEA (60%), VF/VT (24%) and asystole (16%). A temporary surge of PEA was observed between 10 and 15 min due to a doubling of the transition rate from ROSC to PEA (i.e. 're-arrests'). The prevalence of sustained ROSC reached an asymptotic value of 30% at 20 min. Simulation suggests that doubling the transition rate from PEA to ROSC and halving the relapse rate might increase the prevalence of sustained ROSC to 50%.

CONCLUSION

Children and adolescents who received CPR were prone to re-arrest between 10 and 15 min after start of CPR efforts. If the rate of PEA to ROSC transition could be increased and the rate of re-arrests reduced, the overall survival rate may improve.

A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions

GOAL

Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally demanding, and their accuracy could be improved. The objective of this work was to introduce a computationally efficient algorithm for shock decision during piston-driven CPR with increased accuracy.

METHODS

The study dataset contains 201 shockable and 844 nonshockable ECG segments from 230 cardiac arrest patients treated with the LUCAS-2 mechanical CPR device. Compression artifacts were removed using the state-of-the-art adaptive filters, and shock/no-shock discrimination features were extracted from the stationary wavelet transform analysis of the filtered ECG, and fed to a support vector machine (SVM) classifier. Quasi-stratified patient wise nested cross-validation was used for feature selection and SVM hyperparameter optimization. The procedure was repeated 50 times to statistically characterize the results.

RESULTS

Best results were obtained for a six-feature classifier with mean (standard deviation) sensitivity, specificity, and total accuracy of 97.5 (0.4), 98.2 (0.4), and 98.1 (0.3), respectively. The algorithm presented a five-fold reduction in computational demands when compared to the best available methods, while improving their balanced accuracy by 3 points.

CONCLUSIONS

The accuracy of the best available methods was improved while drastically reducing the computational demands.

SIGNIFICANCE

An efficient and accurate method for shock decisions during mechanical CPR is now available to improve therapy and contribute to increase cardiac arrest survival.

A Multistage Algorithm for ECG Rhythm Analysis During Piston-Driven Mechanical Chest Compressions

GOAL

An accurate rhythm analysis during cardiopulmonary resuscitation (CPR) would contribute to increase the survival from out-of-hospital cardiac arrest. Piston-driven mechanical compression devices are frequently used to deliver CPR. The objective of this paper was to design a method to accurately diagnose the rhythm during compressions delivered by a piston-driven device.

METHODS

Data was gathered from 230 out-of-hospital cardiac arrest patients treated with the LUCAS 2 mechanical CPR device. The dataset comprised 201 shockable and 844 nonshockable ECG segments, whereof 270 were asystole (AS) and 574 organized rhythm (OR). A multistage algorithm (MSA) was designed, which included two artifact filters based on a recursive least squares algorithm, a rhythm analysis algorithm from a commercial defibrillator, and an ECG-slope-based rhythm classifier. Data was partitioned randomly and patient-wise into training (60%) and test (40%) for optimization and validation, and statistically meaningful results were obtained repeating the process 500 times.

RESULTS

The mean (standard deviation) sensitivity (SE) for shockable rhythms, specificity (SP) for nonshockable rhythms, and the total accuracy of the MSA solution were: 91.7 (6.0), 98.1 (1.1), and 96.9 (0.9), respectively. The SP for AS and OR were 98.0 (1.7) and 98.1 (1.4), respectively.

CONCLUSIONS

The SE/SP were above the 90%/95% values recommended by the American Heart Association for shockable and nonshockable rhythms other than sinus rhythm, respectively.

SIGNIFICANCE

It is possible to accurately diagnose the rhythm during mechanical chest compressions and the results considerably improve those obtained by previous algorithms.

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.

A method to differentiate between ventricular fibrillation and asystole during chest compressions using artifact-corrupted ECG alone

In recent years, numerous adaptive filtering techniques have been developed to suppress the chest compression (CC) artifact for reliable analysis of the electrocardiogram (ECG) rhythm without CC interruption. Unfortunately, the result of rhythm diagnosis during CCs is still unsatisfactory in many studies. The misclassification between corrupted asystole (ASY) and corrupted ventricular fibrillation (VF) is generally regarded as one of the major reasons for the poor performance of reported methods. In order to improve the diagnosis of VF/ASY corrupted by CCs, a novel method combining a least mean-square (LMS) filter and an amplitude spectrum area (AMSA) analysis was developed based only on the analysis of the surface of the corrupted ECG episode. This method was tested on 253 VF and 160 ASY ECG samples from subjects who experienced cardiac arrest using a porcine model and was compared with six other algorithms. The validation results indicated that this method, which yielded a satisfactory result with a sensitivity of 93.3%, a specificity of 96.3% and an accuracy of 94.8%, is superior to the other reported techniques. After improvement using the human ECG records in real cardiopulmonary resuscitation (CPR) scenarios, the algorithm is promising for corrupted VF/ASY detection with no hardware alterations in clinical practice.

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.

An investigation of thrust, depth and the impedance cardiogram as measures of cardiopulmonary resuscitation efficacy in a porcine model of cardiac arrest

OBJECTIVE

Optimising the depth and rate of applied chest compressions following out of hospital cardiac arrest is crucial in maintaining end organ perfusion and improving survival. The impedance cardiogram (ICG) measured via defibrillator pads produces a characteristic waveform during chest compressions with the potential to provide feedback on cardiopulmonary resuscitation (CPR) and enhance performance. The objective of this pre-clinical study was to investigate the relationship between mechanical and physiological markers of CPR efficacy in a porcine model and examine the strength of correlation between the ICG amplitude, compression depth and end-tidal CO2 (ETCO2).

METHODS

Two experiments were performed using 24 swine (12 per experiment). For experiment 1, ventricular fibrillation (VF) was induced and mechanical CPR commenced at varying thrusts (0-60 kg) for 2 min intervals. Chest compression depth was recorded using a Philips QCPR device with additional recording of invasive physiological parameters: systolic blood pressure, ETCO2, cardiac output and carotid flow. For experiment 2, VF was induced and mechanical CPR commenced at varying depths (0-5 cm) for 2 min intervals. The ICG was recorded via defibrillator pads attached to the animal's sternum and connected to a Heartsine 500 P defibrillator. ICG amplitude, chest compression depth, systolic blood pressure and ETCO2 were recorded during each cycle. In both experiments the within-animal correlation between the measured parameters was assessed using a mixed effect model.

RESULTS

In experiment 1 moderate within-animal correlations were observed between physiological parameters and compression depth (r=0.69-0.77) and thrust (r=0.66-0.82). A moderate correlation was observed between compression depth and thrust (r=0.75). In experiment 2 a strong within-animal correlation and moderate overall correlations were observed between ICG amplitude and compression depth (r=0.89, r=0.79) and ETCO2 (r=0.85, r=0.64).

CONCLUSION

In this porcine model of induced cardiac arrest moderate within animal correlations were observed between mechanical and physiological markers of chest compression efficacy demonstrating the challenge in utilising a single mechanical metric to quantify chest compression efficacy. ICG amplitude demonstrated strong within animal correlations with compression depth and ETCO2 suggesting its potential utility to provide CPR feedback in the out of hospital setting to improve performance.

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.

Reliability and accuracy of the thoracic impedance signal for measuring cardiopulmonary resuscitation quality metrics

AIM

To determine the accuracy and reliability of the thoracic impedance (TI) signal to assess cardiopulmonary resuscitation (CPR) quality metrics.

METHODS

A dataset of 63 out-of-hospital cardiac arrest episodes containing the compression depth (CD), capnography and TI signals was used. We developed a chest compression (CC) and ventilation detector based on the TI signal. TI shows fluctuations due to CCs and ventilations. A decision algorithm classified the local maxima as CCs or ventilations. Seven CPR quality metrics were computed: mean CC-rate, fraction of minutes with inadequate CC-rate, chest compression fraction, mean ventilation rate, fraction of minutes with hyperventilation, instantaneous CC-rate and instantaneous ventilation rate. The CD and capnography signals were accepted as the gold standard for CC and ventilation detection respectively. The accuracy of the detector was evaluated in terms of sensitivity and positive predictive value (PPV). Distributions for each metric computed from the TI and from the gold standard were calculated and tested for normality using one sample Kolmogorov-Smirnov test. For normal and not normal distributions, two sample t-test and Mann-Whitney U test respectively were applied to test for equal means and medians respectively. Bland-Altman plots were represented for each metric to analyze the level of agreement between values obtained from the TI and gold standard.

RESULTS

The CC/ventilation detector had a median sensitivity/PPV of 97.2%/97.7% for CCs and 92.2%/81.0% for ventilations respectively. Distributions for all the metrics showed equal means or medians, and agreements >95% between metrics and gold standard was achieved for most of the episodes in the test set, except for the instantaneous ventilation rate.

CONCLUSION

With our data, the TI can be reliably used to measure all the CPR quality metrics proposed in this study, except for the instantaneous ventilation rate.

Photoplethysmography-based algorithm for detection of cardiogenic output during cardiopulmonary resuscitation

Detecting return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation (CPR) is challenging, time consuming, and requires interrupting chest compressions. Based on automated-CPR porcine data, we have developed an algorithm to support ROSC detection, which detects cardiogenic output during chest compressions via a photoplethysmography (PPG) signal. The algorithm can detect palpable and impalpable spontaneous pulses. A compression-free PPG signal which estimates the spontaneous pulse waveform, was obtained by subtracting the compression component, modeled by a harmonic series. The fundamental frequency of this series was the compression rate derived from the transthoracic impedance signal measured between the defibrillation pads. The amplitudes of the harmonic components were obtained via a least mean-square algorithm. The frequency spectrum of the compression-free PPG signal was estimated via an autoregressive model, and the relationship between the spectral peaks was analyzed to identify the pulse rate (PR). Resumed cardiogenic output could also be detected from a decrease in the baseline of the PPG signal, presumably caused by a redistribution of blood volume to the periphery. The algorithm indicated cardiogenic output when a PR or a redistribution of blood volume was detected. The algorithm indicated cardiogenic output with 94% specificity and 69% sensitivity compared to the retrospective ROSC detection of nine clinicians. Results showed that ROSC detection can be supported by combining the compression-free PPG signal with an indicator based on the detected PR and redistribution of blood volume.

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.

Automatic detection of chest compressions for the assessment of CPR-quality parameters

AIM

Accurate chest compression detection is key to evaluate cardiopulmonary resuscitation (CPR) quality. Two automatic compression detectors were developed, for the compression depth (CD), and for the thoracic impedance (TI). The objective was to evaluate their accuracy for compression detection and for CPR quality assessment.

METHODS

Compressions were manually annotated using the force and ECG in 38 out-of-hospital resuscitation episodes, comprising 869 min and 67,402 compressions. Compressions were detected using a negative peak detector for the CD. For the TI, an adaptive peak detector based on the amplitude and duration of TI fluctuations was used. Chest compression rate (CC-rate) and chest compression fraction (CCF) were calculated for the episodes and for every minute within each episode. CC-rate for rescuer feedback was calculated every 8 consecutive compressions.

RESULTS

The sensitivity and positive predictive value were 98.4% and 99.8% using CD, and 94.2% and 97.4% using TI. The mean CCF and CC-rate obtained from both detectors showed no significant differences with those obtained from the annotations (P>0.6). The Bland-Altman analysis showed acceptable 95% limits of agreement between the annotations and the detectors for the per-minute CCF, per-minute CC-rate, and CC-rate for feedback. For the detector based on TI, only 3.7% of CC-rate feedbacks had an error larger than 5%.

CONCLUSION

Automatic compression detectors based on the CD and TI signals are very accurate. In most cases, episode review could safely rely on these detectors without resorting to manual review. Automatic feedback on rate can be accurately done using the impedance channel.

Removal of cardiopulmonary resuscitation artifacts with an enhanced adaptive filtering method: an experimental trial

Current automated external defibrillators mandate interruptions of chest compression to avoid the effect of artifacts produced by CPR for reliable rhythm analyses. But even seconds of interruption of chest compression during CPR adversely affects the rate of restoration of spontaneous circulation and survival. Numerous digital signal processing techniques have been developed to remove the artifacts or interpret the corrupted ECG with promising result, but the performance is still inadequate, especially for nonshockable rhythms. In the present study, we suppressed the CPR artifacts with an enhanced adaptive filtering method. The performance of the method was evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering the CPR corrupted ECG signals. The dataset comprised 283 segments of shockable and 280 segments of nonshockable ECG signals during CPR recorded from 22 adult pigs that experienced prolonged cardiac arrest. For the unfiltered signals, the sensitivity and specificity were 99.3% and 46.8%, respectively. After filtering, a sensitivity of 93.3% and a specificity of 96.0% were achieved. This animal trial demonstrated that the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR.

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

Survival from out-of-hospital cardiac arrest depends largely on two factors: early cardiopulmonary resuscitation (CPR) and early defibrillation. CPR must be interrupted for a reliable automated rhythm analysis because chest compressions induce artifacts in the ECG. Unfortunately, interrupting CPR adversely affects survival. In the last twenty years, research has been focused on designing methods for analysis of ECG during chest compressions. Most approaches are based either on adaptive filters to remove the CPR artifact or on robust algorithms which directly diagnose the corrupted ECG. In general, all the methods report low specificity values when tested on short ECG segments, but how to evaluate the real impact on CPR delivery of continuous rhythm analysis during CPR is still unknown. Recently, researchers have proposed a new methodology to measure this impact. Moreover, new strategies for fast rhythm analysis during ventilation pauses or high-specificity algorithms have been reported. Our objective is to present a thorough review of the field as the starting point for these late developments and to underline the open questions and future lines of research to be explored in the following years.

Effects of pre-training using serious game technology on CPR performance--an exploratory quasi-experimental transfer study

BACKGROUND

Multiplayer virtual world (MVW) technology creates opportunities to practice medical procedures and team interactions using serious game software. This study aims to explore medical students' retention of knowledge and skills as well as their proficiency gain after pre-training using a MVW with avatars for cardio-pulmonary resuscitation (CPR) team training.

METHODS

Three groups of pre-clinical medical students, n = 30, were assessed and further trained using a high fidelity full-scale medical simulator: Two groups were pre-trained 6 and 18 months before assessment. A reference control group consisting of matched peers had no MVW pre-training. The groups consisted of 8, 12 and 10 subjects, respectively. The session started and ended with assessment scenarios, with 3 training scenarios in between. All scenarios were video-recorded for analysis of CPR performance.

RESULTS

The 6 months group displayed greater CPR-related knowledge than the control group, 93 (±11)% compared to 65 (±28)% (p < 0.05), the 18 months group scored in between (73 (±23)%).At start the pre-trained groups adhered better to guidelines than the control group; mean violations 0.2 (±0.5), 1.5 (±1.0) and 4.5 (±1.0) for the 6 months, 18 months and control group respectively. Likewise, in the 6 months group no chest compression cycles were delivered at incorrect frequencies whereas 54 (±44)% in the control group (p < 0.05) and 44 (±49)% in 18 months group where incorrectly paced; differences that disappeared during training.

CONCLUSIONS

This study supports the beneficial effects of MVW-CPR team training with avatars as a method for pre-training, or repetitive training, on CPR-skills among medical students.