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 C...
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| Veröffentlicht in: | Computers in biology and medicine Jg. 172; S. 108180 |
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Elsevier Ltd
01.04.2024
Elsevier Limited |
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| Abstract | 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.
•Current automated external defibrillator require CPR pauses to determine whether a heart rhythm is shockable.•We developed a novel approach to remove cardiopulmonary resuscitation (CPR) artifacts in ECG without CPR pauses.•Using a cascade of CNNED to remove CPR artifacts significantly improved shockable versus non-shockable rhythm decisions. |
|---|---|
| AbstractList | 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.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. 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. AbstractDelivery 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. 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. •Current automated external defibrillator require CPR pauses to determine whether a heart rhythm is shockable.•We developed a novel approach to remove cardiopulmonary resuscitation (CPR) artifacts in ECG without CPR pauses.•Using a cascade of CNNED to remove CPR artifacts significantly improved shockable versus non-shockable rhythm decisions. |
| ArticleNumber | 108180 |
| Author | Chon, K.H. Nejad, Mahdi Pirayesh Shirazi Kargin, Vadym Valentine, Matt Hicks, David Hajeb-M, Shirin |
| Author_xml | – sequence: 1 givenname: Mahdi Pirayesh Shirazi surname: Nejad fullname: Nejad, Mahdi Pirayesh Shirazi email: mahdi.pirayesh_shirazi_nejad@uconn.edu organization: Biomedical engineering department, University of Connecticut, Storrs, CT, 06269, USA – sequence: 2 givenname: Vadym surname: Kargin fullname: Kargin, Vadym email: vkargin@defibtech.com organization: Defibtech, LLC, Guilford, CT, 06437, USA – sequence: 3 givenname: Shirin orcidid: 0000-0002-9563-1627 surname: Hajeb-M fullname: Hajeb-M, Shirin email: shirin.hajeb@uconn.edu organization: Biomedical engineering department, University of Connecticut, Storrs, CT, 06269, USA – sequence: 4 givenname: David surname: Hicks fullname: Hicks, David email: dhicks@defibtech.com organization: Defibtech, LLC, Guilford, CT, 06437, USA – sequence: 5 givenname: Matt surname: Valentine fullname: Valentine, Matt email: mvalentine@defibtech.com organization: Defibtech, LLC, Guilford, CT, 06437, USA – sequence: 6 givenname: K.H. orcidid: 0000-0002-4422-4837 surname: Chon fullname: Chon, K.H. email: ki.chon@uconn.edu organization: Biomedical engineering department, University of Connecticut, Storrs, CT, 06269, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38452474$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_resplu_2024_100740 crossref_primary_10_1016_j_resuscitation_2024_110327 crossref_primary_10_1016_j_compbiomed_2025_110282 crossref_primary_10_1016_j_resplu_2024_100832 crossref_primary_10_1016_j_resplu_2025_101070 crossref_primary_10_1016_j_compbiomed_2024_109062 |
| Cites_doi | 10.1016/j.resuscitation.2006.05.017 10.1016/S0300-9572(01)00407-5 10.1016/j.resuscitation.2009.09.003 10.1016/j.eswa.2022.117499 10.1109/TBME.2018.2878910 10.3390/s21248210 10.1016/j.resuscitation.2007.08.002 10.1016/j.resuscitation.2003.12.019 10.1109/TBME.2016.2564642 10.1109/TBME.2008.2003107 10.1109/TBME.2008.2010329 10.1161/JAHA.120.019065 10.1097/CCM.0b013e31818a7fbf 10.1016/j.resuscitation.2021.01.003 10.3390/s23094500 10.1161/01.CIR.101.23.e215 10.1109/TBME.2007.902235 10.1007/s13246-016-0425-2 10.1155/2014/140438 10.1016/j.resuscitation.2010.02.031 10.1016/S0300-9572(02)00047-3 10.3390/s21124105 |
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| Keywords | Sudden cardiac arrest Defibrillatory shock Convolutional Neural Network Encoder and decoder Cardiopulmonary resuscitation Electrocardiogram Ventricular tachycardia Ventricular fibrillation |
| Language | English |
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| Snippet | Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to... AbstractDelivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However,... |
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| SubjectTerms | Accuracy Algorithms Arrhythmias, Cardiac - diagnosis Artificial neural networks Automation Cardiac arrest Cardiac arrhythmia Cardiopulmonary resuscitation Cardiopulmonary Resuscitation - methods Classification Contamination Convolutional Neural Network CPR Decision analysis Decision making Defibrillators Defibrillatory shock EKG Electrocardiogram Electrocardiography Electrocardiography - methods Encoder and decoder Encoders-Decoders Fibrillation Heart rate Humans Internal Medicine Lifesaving Neural networks Other Performance evaluation Reference signals Reproducibility of Results Resuscitation Rhythm Segments Shock Signal processing Sudden cardiac arrest Tachycardia Tachycardia, Ventricular Test sets Ventricle Ventricular Fibrillation Ventricular tachycardia |
| Title | Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs |
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