Detection of ventricular fibrillation in the presence of cardiopulmonary resuscitation artefacts

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.

Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs

Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in CPR for accurate analysis heart rhythms. In this study, we propose a novel Convolutional Neural Network-based Encoder-Decoder (CNNED) structure with a shock advisory algorithm to improve the accuracy and reliability of shock versus non-shock decision-making without CPR pause in OHCA scenarios. Our approach employs a cascade of CNNEDs in conjunction with an AED shock advisory algorithm to process the ECG data for shock decisions. Initially, a CNNED trained on an equal number of shockable and non-shockable rhythms is used to filter the CPR-contaminated data. The resulting filtered signal is then fed into a second CNNED, which is trained on imbalanced data more tilted toward the specific rhythm being analyzed. A reliable shock versus non-shock decision is made when both classifiers from the cascade structure agree, while segments with conflicting classifications are labeled as indeterminate, indicating the need for additional segments to analyze. To evaluate our approach, we generated CPR-contaminated ECG data by combining clean ECG data with 52 CPR samples. We used clean ECG data from the CUDB, AFDB, SDDB, and VFDB databases, to which 52 CPR artifact cases were added, while a separate test set provided by the AED manufacturer Defibtech LLC was used for performance evaluation. The test set comprised 20,384 non-shockable CPR-contaminated segments from 392 subjects, as well as 3744 shockable CPR-contaminated samples from 41 subjects with coarse ventricular fibrillation (VF) and 31 subjects with rapid ventricular tachycardia (rapid VT). We observed improvements in rhythm analysis using our proposed cascading CNNED structure when compared to using a single CNNED structure. Specifically, the specificity of the proposed cascade of CNNED structure increased from 99.14% to 99.35% for normal sinus rhythm and from 96.45% to 97.22% for other non-shockable rhythms. Moreover, the sensitivity for shockable rhythm detection increased from 90.90% to 95.41% for ventricular fibrillation and from 82.26% to 87.66% for rapid ventricular tachycardia. These results meet the performance thresholds set by the American Heart Association and demonstrate the reliable and accurate analysis of heart rhythms during CPR using only ECG data without the need for CPR interruptions or a reference signal.

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.

Prediction of Prehospital Change of the Cardiac Rhythm From Nonshockable to Shockable in Out-of-Hospital Patients With Cardiac Arrest: A Post Hoc Analysis of a Nationwide, Multicenter, Prospective Registry

Background

Predicting a spontaneous rhythm change from nonshockable to shockable before hospital arrival in patients with out‐of‐hospital cardiac arrest can help emergency medical services develop better strategies for prehospital treatment. The aim of this study was to identify predictors of spontaneous rhythm change before hospital arrival in patients with out‐of‐hospital cardiac arrest and develop a predictive scoring system.

Methods and Results

We retrospectively reviewed data of eligible patients with out‐of‐hospital cardiac arrest with an initial nonshockable rhythm registered in a nationwide registry between June 2014 and December 2017. We performed a multivariable analysis using a Cox proportional hazards model to identify predictors of a spontaneous rhythm change, and a ridge regression model for predicting it. The data of 25 804 patients were analyzed (derivation cohort, n=17 743; validation cohort, n=8061). The rhythm change event rate was 4.1% (724/17 743) in the derivation cohort, and 4.0% (326/8061) in the validation cohorts. Age, sex, presence of a witness, initial rhythm, chest compression by a bystander, shock with an automated external defibrillator by a bystander, and cause of the cardiac arrest were all found to be independently associated with spontaneous rhythm change before hospital arrival. Based on this finding, we developed and validated the Rhythm Change Before Hospital Arrival for Nonshockable score. The Harrell’s concordance index values of the score were 0.71 and 0.67 in the internal and external validations, respectively.

Conclusions

Seven factors were identified as predictors of a spontaneous rhythm change from nonshockable to shockable before hospital arrival. We developed and validated a score to predict rhythm change before hospital arrival.

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.

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%).

Analyze Whilst Compressing algorithm for detection of ventricular fibrillation during CPR: A comparative performance evaluation for automated external defibrillators

OBJECTIVE

The aim of this study was to present new combination of algorithms for rhythm analysis during cardiopulmonary resuscitation (CPR) in automated external defibrillators (AED), called Analyze Whilst Compressing (AWC), designed for decreasing pre-shock pause and early stopping of chest compressions (CC) for treating refibrillation.

METHODS

Two stages for AED rhythm analysis were presented, namely, "Standard Analysis Stage" (conventional shock-advisory analysis run over 5 s after CC interruption every two minutes) and "AWC Stage" (two-step sequential analysis process during CPR). AWC steps were run in presence of CC (Step1), and if shockable rhythm was detected then a reconfirmation step was run in absence of CC (Step2, analysis duration 5 s).

RESULTS

In total 16,057 ECG strips from 2916 out-of-hospital cardiac arrest (OHCA) patients treated with AEDs (DEFIGARD TOUCH7, Schiller Médical, France) were subjected patient-wise to AWC training (8559 strips, 1604 patients) and validation (7498 strips, 1312 patients). Considering validation results, "Standard Analysis Stage" presented ventricular fibrillation (VF) sensitivity Se = 98.3% and non-shockable rhythm specificity Sp>99%; "AWC Stage" decision after Step2 reconfirmation achieved Se = 92.1%, Sp>99%.

CONCLUSION

AWC presented similar performances to other AED algorithms during CPR, fulfilling performance goals recommended by standards. AWC provided advances in the challenge for improving CPR quality by: (i) not interrupting chest compressions for prevalent part of non-shockable rhythms (66-83%); (ii) minimizing pre-shock pause for 92.1% of VF patients. AWC required hands-off reconfirmation in 34.4% of cases. Reconfirmation was also common limitation of other reported algorithms (25.7-100%) although following different protocols for triggering chest compression resumption and shock delivery.

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.

Subsequent Shockable Rhythm During Out-of-Hospital Cardiac Arrest in Children With Initial Non-Shockable Rhythms: A Nationwide Population-Based Observational Study

BACKGROUND

The effect of a subsequent treated shockable rhythm during cardiopulmonary resuscitation on the outcome of children who suffer out-of-hospital cardiac arrest with initial nonshockable rhythm is unclear. We hypothesized that subsequent treated shockable rhythm in children with out-of-hospital cardiac arrest would improve survival with favorable neurological outcomes (Cerebral Performance Category scale 1-2).

METHODS AND RESULTS

From the All-Japan Utstein Registry, we analyzed the records of 12 402 children (aged <18 years) with out-of-hospital cardiac arrest and initial nonshockable rhythms. Patients were divided into 2 cohorts: subsequent treated shockable rhythm (YES; n=239) and subsequent treated shockable rhythm (NO; n=12 163). The rate of 1-month cerebral performance category 1 to 2 in the subsequent treated shockable rhythm (YES) cohort was significantly higher when compared to the subsequent treated shockable rhythm (NO) cohort (4.6% [11 of 239] vs 1.3% [155 of 12 163]; adjusted odds ratio, 2.90; 95% CI, 1.42-5.36; all P<0.001). In the subsequent treated shockable rhythm (YES) cohort, the rate of 1-month cerebral performance category 1 to 2 decreased significantly as time to shock delivery increased (17.7% [3 of 17] for patients with shock-delivery time 0-9 minutes, 7.3% [8 of 109] for 10-19 minutes, and 0% [0 of 109] for 20-59 minutes; P<0.001 [for trend]). Age-stratified outcomes showed no significant differences between the 2 cohorts in the group aged <7 years old: 1.3% versus 1.4%, P=0.62.

CONCLUSIONS

In children with out-of-hospital cardiac arrest and initial nonshockable rhythms, subsequent treated shockable rhythm was associated with improved 1-month survival with favorable neurological outcomes. In the cohort of older children (7-17 years), these outcomes worsened as time to shock delivery increased.

A new method without reference channels used for ventricular fibrillation detection during cardiopulmonary resuscitation

Ventricular fibrillation (VF) is observed as the initial rhythm in the majority of patients suffering from sudden cardiac arrest. It is vitally important to accurately recognize the initial VF rhythm and then implement electrical defibrillation. However, artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) make the VF detection algorithms utilized by current automated external defibrillators (AEDs) unreliable. CPR must be traditionally interrupted for a reliable diagnosis. However, interruptions in chest compression have a deleterious effect on the success of defibrillation. The elimination of the CPR artifacts would enable compressions to continue during AED VF detection and thereby increase the likelihood of resuscitation success. We have estimated a model of this artifact by adaptively incorporating noise-assisted multivariate empirical mode decomposition (NA-MEMD) and least mean squares (LMS) and then removing the artifact from the corrupted ECGs. The simulation experiment indicated that the CPR artifact could be accurately modeled without any reference channels. We constructed a BP neural network to evaluate the results. A total of 372 VF and 645 normal sinus rhythm (SR) ECG samples were included in the analysis, and 24 CPR artifact signals were used to construct corrupted ECGs. The results indicated that at different SNR levels ranging from 0 to -12 dB, the sensitivity and specificity were always above 95 and 80 %, respectively.

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.

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.

A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation

AIMS

Accurate ventricular fibrillation (VF) waveform analysis usually requires rescuers to discontinue cardiopulmonary resuscitation (CPR). However, prolonged "hands-off" time has a deleterious impact on the outcome. We developed a new filter technique that could clean the CPR artifacts and help preserve the shockability index of VF METHODS: We analyzed corrupted ECGs, which were constructed by randomly adding different scaled CPR artifacts to the VF waveforms. A newly developed algorithm was used to identify the CPR fluctuations. The algorithm contained two steps. First, decomposing the raw data by empirical mode decomposition (EMD) into several intrinsic mode fluctuations (IMFs) and combining the dominant IMFs to reconstruct a new signal. Second, calculating each CPR cycle frequency from the new signal and fitting the new signal to the original corrupted ECG by least square mean (LSM) method to derive the CPR artifacts. The estimated VF waveform was derived by subtraction of the CPR artifacts from the corrupted ECG. We then performed amplitude spectrum analysis (AMSA) for original VF, corrupted ECG and estimated VF.

RESULTS

A total of 150 OHCA subjects with initial VF rhythm were included for analysis. Ten CPR artifacts signals were used to construct corrupted ECG. Even though the correlations of AMSA between the corrupted ECG vs. the original VF and the estimated VF vs. the original VF are all high (all p<0.001), the values of AMSA were obviously biased in corrupted ECG with wide limits of agreement in Bland-Altman mean-difference plot. ROC analysis of the AMSA in the prediction of defibrillation success showed that the new algorithm could preserve the cut-off AMSA value for CPR artifacts with power ratio to VF from 0 to 6 dB.

CONCLUSION

The new algorithm could efficiently filter the CPR-related artifacts of the VF ECG and preserve the shockability index of the original VF waveform.

An algorithm used for ventricular fibrillation detection without interrupting chest compression

Ventricular fibrillation (VF) is the primary arrhythmic event in the majority of patients suffering from sudden cardiac arrest. Attention has been focused on this particular rhythm since it is recognized that prompt therapy, especially electrical defibrillation, may lead to a successful outcome. However, current versions of automated external defibrillators (AEDs) mandate repetitive interruptions of chest compression for rhythm analyses since artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) preclude reliable electrocardiographic (ECG) rhythm analysis. Yet, repetitive interruptions in chest compression are detrimental to the success of defibrillation. The capability for rhythm analysis without requiring "hands-off" intervals will allow for more effective resuscitation. In this paper, a novel continuous-wavelet-transformation-based morphology consistency evaluation algorithm was developed for the detection of disorganized VF from organized sinus rhythm (SR) without interrupting the ongoing chest compression. The performance of this method was evaluated on both uncorrupted and corrupted ECG signals recorded from AEDs obtained from out-of-hospital victims of cardiac arrest. A total of 232 patients and 31,092 episodes of either VF or SR were accessed, in which 8195 episodes were corrupted by artifacts produced by chest compressions. We also compared the performance of this method with three other established algorithms, including VF filter, spectrum analysis, and complexity measurement. Even though there was a modest decrease in specificity and accuracy when chest compression artifact was present, the performance of this method was still superior to other reported methods for VF detection during uninterrupted CPR.

Performance of an automated external defibrillator during simulated rotor-wing critical care transports

OBJECTIVE

This study aimed to evaluate whether an automated external defibrillator (AED) was accurate enough to analyze the heart rhythm during a simulated rotor wing critical care transport. We hypothesized that AED analysis of the simulated rhythms during a helicopter flight would result in significant errors (i.e., inappropriate shocks, analysis delay).

METHODS

Three commercial AEDs were tested for analyzing the heart rhythm in a helicopter using a manikin and a human volunteer. Ventricular fibrillation (VF), sinus rhythm, and asystole were simulated by using an arrhythmia simulator of the manikin. The intervals from analysis to shock recommendation were collected on a stationary and in-motion helicopter. Sensitivity and specificity of three AEDs were also calculated. Vibration intensities were measured with a digital vibration meter placed on the chest of the manikin/human volunteer both on the stretcher and on the floor of the helicopter.

RESULTS

All AEDs correctly recommended shock delivery for the cardiac rhythms of the manikin. Sensitivity for VF was 100.0% (95% CI 91.2-100.0) and specificity for sinus rhythm and asystole were 100.0% (95% CI 91.2-100.0). Although the recorded ECG rhythms of the volunteer in an in-motion helicopter showed baseline artifacts, all AEDs analyzed the cardiac rhythm of the volunteer correctly and did not recommend shock delivery. On the floor of the helicopter, the median measured vibration intensity was 6.6 m/s(2) (IQR 5.5-7.7 m/s(2)) with significantly less vibrations transmitted to the manikin/human volunteer chest (manikin median 3.1 m/s(2), IQR 2.2-4.0 m/s(2); human volunteer median 0.95 m/s(2), IQR 0.65-1.25 m/s(2)).

CONCLUSION

This study suggested that current AEDs could analyze the heart rhythm correctly during simulated helicopter transport. Further studies using an animal model would be needed before applying to patients.

Cardiopulmonary resuscitation artefact suppression using a Kalman filter and the frequency of chest compressions as the reference signal

AIM

To develop a new method to suppress the artefact generated by chest compressions during cardiopulmonary resuscitation (CPR) using only the frequency of the compressions as additional information.

MATERIALS AND METHODS

The CPR artefact suppression method was developed and tested using a database of 381 ECG records (89 shockable and 292 non-shockable) from 299 patients. All records were extracted from real out-of-hospital cardiac arrest episodes. The suppression method consists of a Kalman filter that uses the frequency of the measured compressions to estimate the artefact and to remove it from the ECG. The performance of the filter was evaluated by comparing the sensitivity and specificity of an automated external defibrillator before and after the artefact suppression.

RESULTS

For the test database, the sensitivity improved from 57.8% (95% confidence interval, 43.3-71.0%) to 93.3% (81.5-98.4%) and the specificity decreased from 92.5% (87.0-95.9%) to 89.1% (83.0-93.3%).

CONCLUSION

For a similar sensitivity, we obtained better specificity than that reported for other methods, although still short of the values recommended by the American Heart Association. The results suggest that the CPR artefact can be accurately modelled using only the frequency of the compressions. This information could be easily acquired through the defibrillator's CPR help pads, with minimal hardware modifications.

Diagnostic performance of a new multifunctional electrocardiograph during uninterrupted chest compressions in cardiac arrest patients

BACKGROUND

External chest compression is considered to play a significant role in cardiopulmonary resuscitation (CPR), but during a rhythm check, chest compressions must be discontinued to avoid artifacts. A new multifunctional electrocardiograph (ECG; Radarcirc) has been developed for use in clinical settings.

METHODS AND RESULTS

The performance of the Radarcirc and conventional ECG (CoECG) during CPR was compared in a single-center, non-randomized, sequential self-controlled study. CPR was performed on 41 out-of-hospital cardiac arrest patients. Cardiac rhythm with and without chest compressions during a rhythm check was measured using leads I and II. When the rhythm changed during CPR, it was measured as another waveform. Fifty ECG recordings were obtained, of which 27 were asystole, 18 pulseless electrical activity, and 5 ventricular fibrillation (VF). The area under the receiver-operating characteristic curve (AUC) for VF was 0.448 (95% confidence interval (CI) 0.274-0.622) for lead II of the CoECG, and 0.797 (95%CI 0.684-0.910) for lead II of the Radarcirc. The AUC for VF was 0.422 (95%CI 0.219-0.626) for lead I of the CoECG, and 0.987 (95%CI 0.975-1.00) for lead I of the Radarcirc.

CONCLUSIONS

Diagnoses based on the data from Radarcirc were more accurate in predicting rhythm during chest compressions than those based on data from the CoECG.

Human ECGs corrupted with real CPR artefacts in an animal model: generating a database to evaluate and refine algorithms for eliminating CPR artefacts

AIM

For the analysis of ECG rhythms during ongoing CPR, single- or two-channel methods have been proposed to eliminate artefacts from the CPR-corrupted ECG. To refine, test and evaluate these algorithms with a realistic data set, we introduce an animal model with which we created an extended database of human ECGs with real CPR artefacts.

MATERIAL AND METHODS

In a pig model real CPR-related artefacts were added to annotated human emergency ECGs. Via a special catheter placed in the oesophagus, ECG sequences (duration>10s) were fed in close to the dead pig's heart. The resulting surface potential was recorded on the thorax without and during ongoing chest compressions, which were monitored using a miniature force sensor.

RESULTS

The animals served as a vehicle for human ECGs, making it possible to create a database in which 918 real human ECG sequences (437 shockable and 481 non-shockable) were corrupted with CPR-induced artefacts. The achieved signal-to-noise ratios (SNR) ranged from -17 to +15 dB, sensitivity was 93.5% and specificity was 50.51%. The fed-in ECG and the uncorrupted surface ECG correlated almost perfectly (r=0.926+/-0.081; n=918), indicating negligible signal distortion due to the dead pig itself.

CONCLUSION

As the generated database includes both the original and the corrupted ECG covering a wide range of SNRs as well as the compression force signal, it provides an extended data set to evaluate the reconstruction performance of CPR artefact-removal algorithms.

The optimal phasic relationship between synchronized shock and mechanical chest compressions

OBJECTIVE

Pauses for shock delivery in chest compressions are detrimental to the success of resuscitation and may be eliminated with the use of mechanical chest compressors. However, the optimal phasic relationship between mechanical chest compression and defibrillation is still unknown. We therefore undertook a study to assess the effects of timing of defibrillation in the mechanical chest compression cycle on the defibrillation threshold (DFT) using a porcine model of cardiac arrest.

METHODS

Ventricular fibrillation was electrically induced and untreated for 10s in 8 domestic pigs weighing between 26 and 30 kg. Mechanical chest compression was then continuously performed for 25s, followed by a biphasic electrical shock which was delivered to the animal at 6 randomized coupling phases, including a control phase, with a pre-determined energy setting. The control phase was chosen at a constant 2s following discontinued chest compression. A novel grouped up-and-down DFT testing protocol was used to compare the success rate at different coupling phases. After a recovery interval of 4 min, the testing sequence was repeated, resulting in a total of 60 test shocks delivered to each animal.

RESULTS

No difference between the delivered shock energy, voltage and current were observed among the 6 study phases. The defibrillation success rate, however, was significantly higher when shocks were delivered in the upstroke phase of mechanical chest compression.

CONCLUSION

Defibrillation efficacy is maximal when electrical shock is delivered in the upstroke phase of mechanical chest compression.

Defibrillation delivered during the upstroke phase of manual chest compression improves shock success

OBJECTIVE

The current standard of manual chest compression during cardiopulmonary resuscitation requires pauses for rhythm analysis and shock delivery. However, interruptions of chest compression greatly decrease the likelihood of successful defibrillations, and significantly better outcomes are reported if this interruption is avoided. We therefore undertook a prospective randomized controlled animal study in an electrically induced ventricular fibrillation pig model to assess the effects of timing of defibrillation on the manual chest compression cycle on the defibrillation threshold.

DESIGN

Prospective, randomized, controlled animal study.

SETTING

University-affiliated research laboratory.

SUBJECTS

Yorkshire-X domestic pigs (Sus scrofa).

INTERVENTIONS

In eight domestic male pigs weighing between 24 and 31 kg, ventricular fibrillation was electrically induced and untreated for 10 secs. Manual chest compression was then performed and continued for 25 secs with the protection of an isolation blanket. The depth and frequency of chest compressions were guided by a cardiopulmonary resuscitation prompter. Animals were randomized to receive a biphasic electrical shock in five different compression phases with a predetermined energy setting. A control phase was chosen at a constant 2 secs after discontinued chest compression. A grouped up-down defibrillation threshold testing protocol was used to compare the success rate at different coupling phases. After a recovery interval of 4 mins, the sequence was repeated for a total of 60 test shocks for each animal.

MEASUREMENTS AND MAIN RESULTS

No difference in coronary perfusion pressure before delivering of the shock was observed among the six study phases. The defibrillation success rate, however, was significantly higher when shocks were delivered in the upstroke phase of manual chest compression.

CONCLUSION

Defibrillation efficacy is maximal when electrical shock is delivered during the upstroke phase of manual chest compression.

Shock advisory system for heart rhythm analysis during cardiopulmonary resuscitation using a single ECG input of automated external defibrillators

Minimum "hands-off" intervals during cardiopulmonary resuscitation (CPR) are required to improve the success rate of defibrillation. In support of such life-saving practice, a shock advisory system (SAS) for automatic analysis of the electrocardiogram (ECG) contaminated by chest compression (CC) artefacts is presented. Ease of use for the automated external defibrillators (AEDs) is aimed and therefore only processing of ECG from usual defibrillation pads is required. The proposed SAS relies on assessment of outstanding components of ECG rhythms and CC artefacts in the time and frequency domain. For this purpose, three criteria are introduced to derive quantitative measures of band-pass filtered CC-contaminated ECGs, combined with three more criteria for frequency-band evaluation of reconstructed ECGs (rECG). The rECGs are derived by specific techniques for CC waves similarity assessment and are reproducing to some extent the underlying ECG rhythms. The rhythm classifier embedded in SAS takes a probabilistic decision designed by statistics on the training dataset. Both training and testing are fully performed on real CC-contaminated strips of 10 s extracted from human ECGs of out-of-hospital cardiac arrest interventions. The testing is done on 172 shockable strips (ventricular fibrillations VF), 371 non-shockable strips (NR) and 330 asystoles (ASYS). The achieved sensitivity of 90.1% meets the AHA performance goal for noise-free VF (>90%). The specificity of 88.5% for NR and 83.3% for ASYS are comparable or even better than accuracy reported in literature. It is important to note that, the aim of this SAS is not to recommend shock delivery but to advice the rescuers to "Continue CPR" or to "Stop CPR and Prepare for Shock" thus minimizing "hands-off" intervals.

Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal

Background

Interruption of cardiopulmonary resuscitation (CPR) impairs the perfusion of the fibrillating heart, worsening the chance for successful defibrillation. Therefore ECG-analysis during ongoing chest compression could provide a considerable progress in comparison with standard analysis techniques working only during "hands-off" intervals.

Methods

For the reduction of CPR-related artifacts in ventricular fibrillation ECG we use a localized version of the coherent line removal algorithm developed by Sintes and Schutz. This method can be used for removal of periodic signals with sufficiently coupled harmonics, and can be adapted to specific situations by optimal choice of its parameters (e.g., the number of harmonics considered for analysis and reconstruction). Our testing was done with 14 different human ventricular fibrillation (VF) ECGs, whose fibrillation band lies in a frequency range of [1 Hz, 5 Hz]. The VF-ECGs were mixed with 12 different ECG-CPR-artifacts recorded in an animal experiment during asystole. The length of each of the ECG-data was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data. VF-to-CPR ratio was chosen as -20 dB, -15 dB, -10 dB, -5 dB, 0 dB, 5 dB and 10 dB. Here -20 dB corresponds to the highest level of CPR-artifacts.

Results

For non-optimized coherent line removal based on signals with a VF-to-CPR ratio of -20 dB, -15 dB, -10 dB, -5 dB and 0 dB, the signal-to-noise gains (SNR-gains) were 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB and 8.0 ± 2.7 (mean ± std, n = 168), respectively. Characteristically, an original VF-to-CPR ratio of -10 dB, corresponds to a variance ratio var(VF):var(CPR) = 1:10. An improvement by 9.5 dB results in a restored VF-to-CPR ratio of -0.5 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.

Discussion

The localized coherent line removal algorithm uses the information of a single ECG channel. In contrast to multi-channel algorithms, no additional information such as thorax impedance, blood pressure, or pressure exerted on the sternum during CPR is required. Predictors of defibrillation success such as mean and median frequency of VF-ECGs containing CPR-artifacts are prone to being governed by the harmonics of the artifacts. Reduction of CPR-artifacts is therefore necessary for determining reliable values for estimators of defibrillation success.

Conclusions

The localized coherent line removal algorithm reduces CPR-artifacts in VF-ECG, but does not eliminate them. Our SNR-improvements are in the same range as offered by multichannel methods of Rheinberger et al., Husoy et al. and Aase et al. The latter two authors dealt with different ventricular rhythms (VF and VT), whereas here we dealt with VF, only. Additional developments are necessary before the algorithm can be tested in real CPR situations.

Strong corruption of electrocardiograms caused by cardiopulmonary resuscitation reduces efficiency of two-channel methods for removing motion artefacts in non-shockable rhythms

AIM

Cardiopulmonary resuscitation (CPR) artefact removal methods provide satisfactory results when the rhythm is shockable but fail on non-shockable rhythms. We investigated the influence of the corruption level on the performance of four different two-channel methods for CPR artefact removal.

MATERIALS AND METHODS

395 artefact-free ECGs and 13 pure CPR artefacts with corresponding blood pressure readings as a reference channel were selected. Using a simplified additive data model we generated CPR-corrupted signals at different signal-to-noise ratio (SNR) levels from -10 to +10 dB. The algorithms were optimized on learning data with respect to SNR improvement and then applied to testing data. Sensitivity and specificity were derived from the shock/no-shock advice of an automated external defibrillator before CPR corruption and after artefact removal.

RESULTS

Sensitivity for the filtered data (>95%) was significantly superior to that for the unfiltered data (76%), p<0.001. However, specificity was similar for the filtered and unfiltered data (<90% vs 89.3%). For large artefacts (-10 dB) specificity decreased below 70%. No important difference in the performance of the four algorithms was found.

CONCLUSION

Using a simplified data model we showed that, when the ECG rhythm is non-shockable, two-channel methods could not reduce CPR artefacts without affecting the rhythm analysis for shock recommendation. The reason could be poor reconstruction when the artefacts are large. However, poor reconstruction was not a hindrance to re-identifying shockable rhythms. Future investigations should both include the refinement of filter methods and also focus on reducing motion artefacts already at the recording stage.

CPR artifact removal in ventricular fibrillation ECG signals using Gabor multipliers

BACKGROUND AND OBJECTIVE

We present an algorithm for discarding cardiopulmonary resuscitation (CPR) components from ventricular fibrillation ECG (VF ECG) signals and establish a method for comparing CPR attenuation on a common dataset. Removing motion artifacts in ECG allows for uninterrupted rhythm analysis and reduces "hands-off" time during resuscitation.

METHODS AND RESULTS

The current approach assumes a multichannel setting where the information of the corrupted ECG is combined with an additional pressure signal in order to estimate the motion artifacts. The underlying algorithm relies on a localized time-frequency transformation, the Gabor transform, that reveals the perturbation components, which, in turn, can be attenuated. The performance of the method is evaluated on a small set of test signals in the form of error analysis and compared to two well-established CPR removal algorithms that use an adaptive filtering system and a state-space model, respectively.

CONCLUSION

We primarily point out the potential of the algorithm for successful artifact removal; however, on account of the limited set of human VF and animal asystole CPR signals, we refrain from a statistical analysis of the efficiency of CPR attenuation. The results encourage further investigations in both the theoretical and the clinical setup.

A least mean-square filter for the estimation of the cardiopulmonary resuscitation artifact based on the frequency of the compressions

Cardiopulmonary resuscitation (CPR) artifacts caused by chest compressions and ventilations interfere with the rhythm diagnosis of automated external defibrillators (AED). CPR must be interrupted for a reliable diagnosis. However, pauses in chest compressions compromise the defibrillation success rate and reduce perfusion of vital organs. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. We have estimated the CPR artifact using only the frequency of the compressions as additional information to model it. Our model of the artifact is adaptively estimated using a least mean-square (LMS) filter. It was tested on 89 shockable and 292 nonshockable ECG samples from real out-of-hospital sudden cardiac arrest episodes. We evaluated the results using the shock advice algorithm of a commercial AED. The sensitivity and specificity were above 95% and 85%, respectively, for a wide range of working conditions of the LMS filter. Our results show that the CPR artifact can be accurately modeled using only the frequency of the compressions. These can be easily registered after small changes in the hardware of the CPR compression pads.

Ventricular fibrillation and defibrillation

Cardiac arrest in children is not often due to a disturbance in rhythm that is amenable to electrical defibrillation, contrary to the situation in adults. When a shockable rhythm is present, defibrillation using an external electric shock applied at an early stage after pre-oxygenation and chest compressions is of proven efficacy. Success at conversion of ventricular fibrillation is dependent on the delay before delivering the shock and defibrillation efficiency, which is itself a function of thoracic impedance, energy dose and waveform.

A method to remove CPR artefacts from human ECG using only the recorded ECG

AIM

To show the possibility of using cardiopulmonary resuscitation (CPR) artefact suppression methods that do not need additional reference signals to model CPR artefacts.

MATERIALS AND METHODS

A CPR suppression method based on a Kalman filter was designed. The artefact was modelled using the fundamental frequency of the compressions, estimated from the spectral analysis of the ECG. Artificial mixtures of human shockable rhythms and CPR artefacts were used to design the algorithm that was then tested on samples obtained from real out-of-hospital cardiac arrest episodes.

RESULTS

The shock/no-shock decision of an automated external defibrillator (AED) was evaluated before and after CPR suppression for 131 shockable and 347 non-shockable samples. The sensitivity improved from 56% (95% CI, 47-64%) to 90% (95% CI, 84-94%). However, the specificity decreased from 91% (95% CI, 87-93%) to 80% (95% CI, 76-84%).

CONCLUSIONS

CPR artefacts can be suppressed using methods based on the analysis of the ECG alone. The hardware of current AEDs does not need to be replaced, although better artefact suppression methods exist for modified AEDs with additional reference channels.