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.

[1]  P. Larsen,et al.  Comparison of ease of use of three automated external defibrillators by untrained lay people. , 2003, Resuscitation.

[2]  J. Nolan,et al.  European Resuscitation Council guidelines for resuscitation 2005. Section 3. Electrical therapies: automated external defibrillators, defibrillation, cardioversion and pacing. , 2005, Resuscitation.

[3]  H. Halperin,et al.  Determination of the noise source in the electrocardiogram during cardiopulmonary resuscitation , 2002, Critical care medicine.

[4]  A. Amann,et al.  Removal of resuscitation artefacts from ventricular fibrillation ECG signals using kalman methods , 2005, Computers in Cardiology, 2005.

[5]  M. Weil,et al.  Electrocardiographic prediction of the success of cardiac resuscitation. , 1999, Critical care medicine.

[6]  TingYu,et al.  Adverse Outcomes of Interrupted Precordial Compression During Automated Defibrillation , 2002 .

[7]  H U Strohmenger,et al.  Analysis of the ventricular fibrillation ECG signal amplitude and frequency parameters as predictors of countershock success in humans. , 1997, Chest.

[8]  Jesus Ruiz,et al.  Detection of ventricular fibrillation in the presence of cardiopulmonary resuscitation artefacts. , 2007, Resuscitation.

[9]  J P Ornato,et al.  Automatic external defibrillators for public access defibrillation: recommendations for specifying and reporting arrhythmia analysis algorithm performance, incorporating new waveforms, and enhancing safety. A statement for health professionals from the American Heart Association Task Force on Automa , 1997, Circulation.

[10]  A. Lazkano,et al.  A variable step size LMS algorithm for the suppression of the CPR artefact from a VF signal , 2005, Computers in Cardiology, 2005.

[11]  P. Steen,et al.  Effects of Interrupting Precordial Compressions on the Calculated Probability of Defibrillation Success During Out-of-Hospital Cardiac Arrest , 2002, Circulation.

[12]  Maaret Castrén,et al.  European Resuscitation Council Guidelines for Resuscitation 2010 Section 2. Adult basic life support and use of automated external defibrillators , 2010, Resuscitation.

[13]  C. Braun,et al.  Adaptive AR modeling of nonstationary time series by means of Kalman filtering , 1998, IEEE Transactions on Biomedical Engineering.

[14]  Roger D. White,et al.  Hands-only (compression-only) cardiopulmonary resuscitation: a call to action for bystander response to adults who experience out-of-hospital sudden cardiac arrest: a science advisory for the public from the American Heart Association Emergency Cardiovascular Care Committee. , 2008, Circulation.

[15]  K. Lindner,et al.  Spectral analysis of ventricular fibrillation and closed-chest cardiopulmonary resuscitation. , 1996, Resuscitation.

[16]  M. Copass,et al.  Changing incidence of out-of-hospital ventricular fibrillation, 1980-2000. , 2002, JAMA.

[17]  A. Lazkano,et al.  A simple effective filtering method for removing CPR caused artefacts from surface ECG signals , 2005, Computers in Cardiology, 2005.

[18]  Jo Kramer-Johansen,et al.  Effects of compression depth and pre-shock pauses predict defibrillation failure during cardiac arrest. , 2006, Resuscitation.

[19]  Irena Jekova,et al.  Bench study of the accuracy of a commercial AED arrhythmia analysis algorithm in the presence of electromagnetic interferences , 2009, Physiological measurement.

[20]  A. Lazkano,et al.  CPR artefact removal from VF signals by means of an adaptive kalman filter using the chest compression frequency as reference signal , 2005, Computers in Cardiology, 2005.

[21]  G. Ewy A new approach for out-of-hospital CPR: a bold step forward. , 2003, Resuscitation.

[22]  P. Steen,et al.  Effects of Interrupting Precordial Compressions on the Calculated Probability of Defibrillation Success During Out-of-Hospital Cardiac Arrest , 2002 .

[23]  G. Perkins,et al.  European Resuscitation Council guidelines for resuscitation 2005. Section 2. Adult basic life support and use of automated external defibrillators. , 2005, Resuscitation.

[24]  A. Hallstrom,et al.  Predicting survival from out-of-hospital cardiac arrest: a graphic model. , 1993, Annals of emergency medicine.

[25]  I Dotsinsky,et al.  Detection of shockable and non-shockable rhythms in presence of CPR artifacts by time-frequency ECG analysis , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[26]  Trygve Eftestøl,et al.  A Least Mean-Square Filter for the Estimation of the Cardiopulmonary Resuscitation Artifact Based on the Frequency of the Compressions , 2009, IEEE Transactions on Biomedical Engineering.

[27]  Greg Sinibaldi,et al.  Incidence of EMS-treated out-of-hospital cardiac arrest in the United States. , 2004, Resuscitation.

[28]  Trygve Eftestøl,et al.  Removal of cardiopulmonary resuscitation artifacts from human ECG using an efficient matching pursuit-like algorithm , 2002, IEEE Transactions on Biomedical Engineering.

[29]  Wanchun Tang,et al.  Adverse effects of interrupting precordial compression during cardiopulmonary resuscitation. , 1997, Critical care medicine.

[30]  Gavin D Perkins,et al.  European Resuscitation Council Guidelines for Resuscitation 2010 Section 4. Adult advanced life support. , 2010, Resuscitation.

[31]  P. Steen,et al.  Delaying defibrillation to give basic cardiopulmonary resuscitation to patients with out-of-hospital ventricular fibrillation: a randomized trial. , 2003, JAMA.

[32]  P. H. Seed,et al.  Section 3 , 2007, Learning Greek with Plato.

[33]  R W Koster,et al.  Out-of-hospital cardiac arrests in Amsterdam and its surrounding areas: results from the Amsterdam resuscitation study (ARREST) in 'Utstein' style. , 1998, Resuscitation.

[34]  Klaus Rheinberger,et al.  Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals , 2008, IEEE Transactions on Biomedical Engineering.

[35]  Richard E. Kerber,et al.  Automatic External Defibrillators for Public Access Defibrillation: Recommendations for Specifying and Reporting Arrhythmia Analysis Algorithm Performance, Incorporating New Waveforms, and Enhancing Safety , 1997, Biomedical instrumentation & technology.

[36]  S. O. Aase,et al.  Feasibility of shock advice analysis during CPR through removal of CPR artefacts from the human ECG. , 2004, Resuscitation.

[37]  Jesus Ruiz,et al.  A method to remove CPR artefacts from human ECG using only the recorded ECG. , 2008, Resuscitation.

[38]  Gavin D. Perkins,et al.  European Resuscitation Council Guidelines for Resuscitation 2005 , 2005 .

[39]  Trygve Eftestøl,et al.  CPR artifact removal from human ECG using optimal multichannel filtering , 2000, IEEE Transactions on Biomedical Engineering.

[40]  P. Steen,et al.  Reducing CPR artefacts in ventricular fibrillation in vitro. , 2001, Resuscitation.