Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology

Background : Pauses in cardiopulmonary resuscitation (CPR) for Automatic External Defibrillator (AED) ECG analysis may adversely affect cardiac arrest resuscitation. Thus, approaches that analyze the ECG rhythm during CPR may improve outcomes. We developed and tested an Analysis During CPR (ADC) algorithm to determine if it would meet the American Heart Association recommended 90% sensitivity for coarse (>0.2 mV peak-peak) ventricular fibrillation (VF) and 95% specificity for non-shockable rhythms. Methods : Defibrillator ECG and impedance recordings from 162 patients were retrospectively gathered from 3 EMS systems. 1047 15-second CPR-artifacted segments (274 coarse VF + 773 non-shockable) were identified for analysis; their artifact and rhythm distributions reflect those found in the 162 patients. Each CPR artifacted segment was paired with an adjacent segment free of CPR artifact for reference. Independent reviewers manually annotated and verified Shock/No-Shock rhythm designations blinded to the ADC determination. The ADC algorithm automatically classified each segment into categories of Shock/No Shock/Pause CPR For Clean Analysis, where the last category is segments recognized by the ADC as too noisy for accurate Shock/No Shock determination. In those situations the device would revert to the current approach of a CPR pause for AED rhythm analysis. Results : Of the 1047 CPR-artifacted segments, the ADC recommended to “Pause CPR For Clean Analysis” in 10% (n=109), including 4.4% of VF segments (12/274) and 12% (97/773) of non-shockable segments. Of the 938 remaining segments, the ADC correctly identified VF in 97% (sensitivity: 255/262) and correctly identified nonshockable rhythms in 96% (specificity: 650/676). Corresponding positive and negative predictive values were 91% and 99% respectively. Conclusions : The ADC is the first algorithm for automated ECG rhythm analysis during ongoing CPR that has been demonstrated to meet the existing AHA sensitivity and specificity recommendations designed for traditional rhythm analysis during hands-off pauses. Incorporation of this algorithm into an AED may eliminate about 90% of analysis pauses without compromising analysis accuracy and in turn may improve the likelihood of resuscitation.

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Shock Advisory System for Heart Rhythm Analysis During Cardiopulmonary Resuscitation Using a Single ECG Input of Automated External Defibrillators

Annals of Biomedical Engineering ◽

10.1007/s10439-009-9885-9 ◽

2010 ◽

Vol 38 (4) ◽

pp. 1326-1336 ◽

Cited By ~ 16

Author(s):

Vessela Krasteva ◽

Irena Jekova ◽

Ivan Dotsinsky ◽

Jean-Philippe Didon

Keyword(s):

Cardiopulmonary Resuscitation ◽

Heart Rhythm ◽

Automated External Defibrillators ◽

Advisory System ◽

Rhythm Analysis

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U SO CARE—The Impact of Cardiac Ultrasound during Cardiopulmonary Resuscitation: A Prospective Randomized Simulator-Based Trial

Journal of Clinical Medicine ◽

10.3390/jcm10225218 ◽

2021 ◽

Vol 10 (22) ◽

pp. 5218

Author(s):

Karim Zöllner ◽

Timur Sellmann ◽

Dietmar Wetzchewald ◽

Heidrun Schwager ◽

Corvin Cleff ◽

...

Keyword(s):

Cardiopulmonary Resuscitation ◽

Diagnostic Accuracy ◽

Treatment Decision ◽

Treatment Decisions ◽

Point Of Care Ultrasound ◽

Correct Treatment ◽

Inappropriate Treatment ◽

Hands On ◽

Rhythm Analysis ◽

The Impact

Background: Actual cardiopulmonary resuscitation (CPR) guidelines recommend point-of-care ultrasound (POCUS); however, data on POCUS during CPR are sparse and conflicting. This randomized trial investigated the effects of POCUS during CPR on team performance and diagnostic accuracy. Methods: Intensive Care and Emergency Medicine residents performed CPR with or without available POCUS in simulated cardiac arrests. The primary endpoint was hands-on time. Data analysis was performed using video recordings. Results: Hands-on time was 89% (87–91) in the POCUS and 92% (89–94) in the control group (difference 3, 95% CI for difference 2–4, p < 0.001). POCUS teams had delayed defibrillator attachments (33 vs. 26 sec, p = 0.017) and first rhythm analysis (74 vs. 52 sec, p = 0.001). Available POCUS was used in 71%. Of the POCUS teams, 3 stated a POCUS-derived diagnosis, with 49 being correct and 42 followed by a correct treatment decision. Four teams made a wrong diagnosis and two made an inappropriate treatment decision. Conclusions: POCUS during CPR resulted in lower hands-on times and delayed rhythm analysis. Correct POCUS diagnoses occurred in 52%, correct treatment decisions in 44%, and inappropriate treatment decisions in 2%. Training on POCUS during CPR should focus on diagnostic accuracy and maintenance of high-quality CPR.

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Abstract 12792: Enhancing the Accuracy of Cardiac Rhythm Analysis in Automated External Defibrillators During Ongoing Cardiopulmonary Resuscitation by Applying a Deep Encoder-Decoder Filtering Model

Circulation ◽

10.1161/circ.144.suppl_2.12792 ◽

2021 ◽

Vol 144 (Suppl_2) ◽

Author(s):

Shirin Hajeb Mohammadalipour ◽

Alicia Cascella ◽

Matt Valentine ◽

Ki Chon

Keyword(s):

Cardiopulmonary Resuscitation ◽

Signal To Noise Ratio ◽

Normal Sinus Rhythm ◽

Motion Artifact ◽

Automated External Defibrillators ◽

Normal Sinus ◽

Before And After ◽

Rhythm Analysis ◽

Distinct Features ◽

Ecg Data

Survival from out-of-hospital cardiac arrests depends on an accurate defibrillatory shock decision during cardiopulmonary resuscitation (CPR). Since chest compressions induce severe motion artifact in the electrocardiogram (ECG), current automatic external defibrillators (AEDs) do not perform CPR during the rhythm analysis period. However, performing continuous CPR is vital and dramatically increases the chance of survival. Hence, we demonstrate a novel application of a deep convolutional neural network encoder-decoder (CNNED) method in suppressing CPR artifact in near real-time using only ECG data. The encoder portion of the CNNED uses the frequency and phase contents derived via time-varying spectral analysis to learn distinct features that are representative of both the ECG signal and CPR artifact. The decoder portion takes the results from the encoder and reconstructs what is perceived as the motion artifact removed ECG data. These procedures are done via multitude of training of CNNED using many different arrhythmia contaminated with CPR. In this study, CPR-contaminated ECGs were generated by combining clean ECG with CPR artifacts from 52 different performers. ECG data from CUDB, VFDB, and SDDB datasets which belong to the Physionet’s Physiobank archive were used to create the training set containing 89,984 14-second ECG segments. The performance of the proposed CNNED was evaluated on a separate test set comprising of 23,816 CPR-contaminated 14-second ECG segments from 458 subjects. The results were evaluated by two metrics: signal-to-noise ratio (SNR), and Defibtech’s AED rhythm analysis algorithm. CNNED resulted in the increase of the mean SNR value from -3 dB to 5.63 dB and 6.3 dB for shockable and non-shockable rhythms, respectively. Comparing Defibtech’s AED rhythm classifier before and after applying CNNED on the CPR-contaminated ECG, the specificity improved from 96.57% to 99.31% for normal sinus rhythm, and from 91.5% to 96.57% for other non-shockable rhythms. The sensitivity of shockable detection also increased from 67.68% to 87.76% for ventricular fibrillation, and from 62.71% to 81.27% for ventricular tachycardia. These results indicate continuous and accurate AED rhythm analysis without stoppage of CPR using only ECG data.

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Practice guideline summary: Reducing brain injury following cardiopulmonary resuscitation

Neurology ◽

10.1212/wnl.0000000000003966 ◽

2017 ◽

Vol 88 (22) ◽

pp. 2141-2149 ◽

Cited By ~ 40

Author(s):

Romergryko G. Geocadin ◽

Eelco Wijdicks ◽

Melissa J. Armstrong ◽

Maxwell Damian ◽

Stephan A. Mayer ◽

...

Keyword(s):

Brain Injury ◽

Cardiopulmonary Resuscitation ◽

Cardiac Rhythm ◽

Pulseless Electrical Activity ◽

Neurologic Outcome ◽

Temperature Management ◽

Evidence Based ◽

Initial Rhythm ◽

Hospital Cardiac Arrest

Objective:To assess the evidence and make evidence-based recommendations for acute interventions to reduce brain injury in adult patients who are comatose after successful cardiopulmonary resuscitation.Methods:Published literature from 1966 to August 29, 2016, was reviewed with evidence-based classification of relevant articles.Results and recommendations:For patients who are comatose in whom the initial cardiac rhythm is either pulseless ventricular tachycardia (VT) or ventricular fibrillation (VF) after out-of-hospital cardiac arrest (OHCA), therapeutic hypothermia (TH; 32–34°C for 24 hours) is highly likely to be effective in improving functional neurologic outcome and survival compared with non-TH and should be offered (Level A). For patients who are comatose in whom the initial cardiac rhythm is either VT/VF or asystole/pulseless electrical activity (PEA) after OHCA, targeted temperature management (36°C for 24 hours, followed by 8 hours of rewarming to 37°C, and temperature maintenance below 37.5°C until 72 hours) is likely as effective as TH and is an acceptable alternative (Level B). For patients who are comatose with an initial rhythm of PEA/asystole, TH possibly improves survival and functional neurologic outcome at discharge vs standard care and may be offered (Level C). Prehospital cooling as an adjunct to TH is highly likely to be ineffective in further improving neurologic outcome and survival and should not be offered (Level A). Other pharmacologic and nonpharmacologic strategies (applied with or without concomitant TH) are also reviewed.

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Rhythm Analysis during Cardiopulmonary Resuscitation: Past, Present, and Future

BioMed Research International ◽

10.1155/2014/386010 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 28

Author(s):

Sofia Ruiz de Gauna ◽

Unai Irusta ◽

Jesus Ruiz ◽

Unai Ayala ◽

Elisabete Aramendi ◽

...

Keyword(s):

Cardiopulmonary Resuscitation ◽

Adaptive Filters ◽

High Specificity ◽

Chest Compressions ◽

Robust Algorithms ◽

Open Questions ◽

Starting Point ◽

Two Factors ◽

Rhythm Analysis ◽

Hospital Cardiac Arrest

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.

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Abstract 253: Trajectory of Ventricular Fibrillation Prognostic Status During Cardiopulmonary Resuscitation

Circulation ◽

10.1161/circ.142.suppl_4.253 ◽

2020 ◽

Vol 142 (Suppl_4) ◽

Author(s):

Jason Coult ◽

Shiv Bhandari ◽

Diya Sashidhar ◽

Thomas Rea ◽

Jennifer E Blackwood ◽

...

Keyword(s):

Cardiac Arrest ◽

Ventricular Fibrillation ◽

Cardiopulmonary Resuscitation ◽

Time Dependent ◽

Probability Of Survival ◽

Predicted Probability ◽

Myocardial Energetics ◽

Rhythm Analysis ◽

Initial Shock ◽

Hospital Cardiac Arrest

Background: Resuscitation from out-of-hospital cardiac arrest (OHCA) due to ventricular fibrillation (VF) typically involves continuous CPR cycles interrupted every 2 minutes for rhythm analysis and potential defibrillation. Quantitative measures of the VF ECG waveform have been proposed to guide therapy for VF arrest because they are associated with myocardial energetics, are dynamic over the course of resuscitation, and predict outcome. However, while VF waveform measures have until recently have required CPR interruption to accurately gauge prognostic status, CPR interruptions are associated with a lower chance of survival. We used a novel waveform measure previously-validated during active CPR to estimate the course of VF status through the 2-minute CPR cycle between consecutive shocks. Methods: We conducted an observational study of patients with VF OHCA who experienced recurrent VF for at least 90 seconds following initial shock. We used the continuous defibrillator ECG to calculate the VF waveform measure as a function of predicted probability of survival-with-intact-neurologic-status at 1-s intervals over the course of resuscitation between shocks. Results: We collected 499 VF ECG segments (≥90 seconds) during CPR from 313 patients. The trajectory of the average prognostic VF measure had a 3-phase time-dependent pattern (Fig. 1). During CPR, the slope of the measure decreased during the initial 25 s of VF (slope = -12%/min) and was relatively flat during the subsequent 65-s interval of VF (slope = +1%/min). Furthermore, slope decreased sharply following the cessation of CPR for rhythm analysis, charge, and shock (slope = -23%/min). Conclusion: On average, a novel VF waveform measure assessed during the scheduled cycle of CPR and rhythm analysis between consecutive shocks was characterized by a period of decline, stabilization, and then decline. Whether these changes in VF status can be used to improve care for individual patients is uncertain.

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A Reliable Method for Rhythm Analysis during Cardiopulmonary Resuscitation

BioMed Research International ◽

10.1155/2014/872470 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 18

Author(s):

U. Ayala ◽

U. Irusta ◽

J. Ruiz ◽

T. Eftestøl ◽

J. Kramer-Johansen ◽

...

Keyword(s):

Cardiopulmonary Resuscitation ◽

High Sensitivity ◽

High Specificity ◽

Support Vector ◽

Test Set ◽

Decision Algorithm ◽

New Approach ◽

Artifact Suppression ◽

Rhythm Analysis ◽

Hospital Cardiac Arrest

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.

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