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Coult J, Yang BY, Kwok H, Kutz JN, Boyle PM, Blackwood J, Rea TD, Kudenchuk PJ. Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest. Circulation 2023; 148:327-335. [PMID: 37264936 DOI: 10.1161/circulationaha.122.063651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 05/08/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND Out-of-hospital cardiac arrest due to shock-refractory ventricular fibrillation (VF) is associated with relatively poor survival. The ability to predict refractory VF (requiring ≥3 shocks) in advance of repeated shock failure could enable preemptive targeted interventions aimed at improving outcome, such as earlier administration of antiarrhythmics, reconsideration of epinephrine use or dosage, changes in shock delivery strategy, or expedited invasive treatments. METHODS We conducted a cohort study of VF out-of-hospital cardiac arrest to develop an ECG-based algorithm to predict patients with refractory VF. Patients with available defibrillator recordings were randomized 80%/20% into training/test groups. A random forest classifier applied to 3-s ECG segments immediately before and 1 minute after the initial shock during cardiopulmonary resuscitation was used to predict the need for ≥3 shocks based on singular value decompositions of ECG wavelet transforms. Performance was quantified by area under the receiver operating characteristic curve. RESULTS Of 1376 patients with VF out-of-hospital cardiac arrest, 311 (23%) were female, 864 (63%) experienced refractory VF, and 591 (43%) achieved functional neurological survival. Total shock count was associated with decreasing likelihood of functional neurological survival, with a relative risk of 0.95 (95% CI, 0.93-0.97) for each successive shock (P<0.001). In the 275 test patients, the area under the receiver operating characteristic curve for predicting refractory VF was 0.85 (95% CI, 0.79-0.89), with specificity of 91%, sensitivity of 63%, and a positive likelihood ratio of 6.7. CONCLUSIONS A machine learning algorithm using ECGs surrounding the initial shock predicts patients likely to experience refractory VF, and could enable rescuers to preemptively target interventions to potentially improve resuscitation outcome.
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Affiliation(s)
- Jason Coult
- Department of Medicine (J.C., T.D.R.), University of Washington, Seattle
| | - Betty Y Yang
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas (B.Y.Y.)
| | - Heemun Kwok
- Department of Applied Mathematics (J.N.K.), University of Washington, Seattle
| | - J Nathan Kutz
- Department of Applied Mathematics (J.N.K.), University of Washington, Seattle
| | - Patrick M Boyle
- Department of Bioengineering (P.M.B.), University of Washington, Seattle
- Institute for Stem Cell and Regenerative Medicine (P.M.B.), University of Washington, Seattle
- Center for Cardiovascular Biology (P.M.B.), University of Washington, Seattle
| | - Jennifer Blackwood
- Emergency Medical Services Division, Public Health - Seattle & King County, Seattle, WA (J.B., T.D.R.)
| | - Thomas D Rea
- Department of Medicine (J.C., T.D.R.), University of Washington, Seattle
- Emergency Medical Services Division, Public Health - Seattle & King County, Seattle, WA (J.B., T.D.R.)
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Zuo F, Dai C, Wei L, Gong Y, Yin C, Li Y. Real-time amplitude spectrum area estimation during chest compression from the ECG waveform using a 1D convolutional neural network. Front Physiol 2023; 14:1113524. [PMID: 37153217 PMCID: PMC10157479 DOI: 10.3389/fphys.2023.1113524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/10/2023] [Indexed: 05/09/2023] Open
Abstract
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.
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Affiliation(s)
- Feng Zuo
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Chenxi Dai
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Liang Wei
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Yushun Gong
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Changlin Yin
- Department of Intensive Care, Southwest Hospital, Army Medical University, Chongqing, China
| | - Yongqin Li
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
- *Correspondence: Yongqin Li,
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Brooks SC, Clegg GR, Bray J, Deakin CD, Perkins GD, Ringh M, Smith CM, Link MS, Merchant RM, Pezo-Morales J, Parr M, Morrison LJ, Wang TL, Koster RW, Ong MEH. Optimizing Outcomes After Out-of-Hospital Cardiac Arrest With Innovative Approaches to Public-Access Defibrillation: A Scientific Statement From the International Liaison Committee on Resuscitation. Circulation 2022; 145:e776-e801. [PMID: 35164535 DOI: 10.1161/cir.0000000000001013] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Out-of-hospital cardiac arrest is a global public health issue experienced by ≈3.8 million people annually. Only 8% to 12% survive to hospital discharge. Early defibrillation of shockable rhythms is associated with improved survival, but ensuring timely access to defibrillators has been a significant challenge. To date, the development of public-access defibrillation programs, involving the deployment of automated external defibrillators into the public space, has been the main strategy to address this challenge. Public-access defibrillator programs have been associated with improved outcomes for out-of-hospital cardiac arrest; however, the devices are used in <3% of episodes of out-of-hospital cardiac arrest. This scientific statement was commissioned by the International Liaison Committee on Resuscitation with 3 objectives: (1) identify known barriers to public-access defibrillator use and early defibrillation, (2) discuss established and novel strategies to address those barriers, and (3) identify high-priority knowledge gaps for future research to address. The writing group undertook systematic searches of the literature to inform this statement. Innovative strategies were identified that relate to enhanced public outreach, behavior change approaches, optimization of static public-access defibrillator deployment and housing, evolved automated external defibrillator technology and functionality, improved integration of public-access defibrillation with existing emergency dispatch protocols, and exploration of novel automated external defibrillator delivery vectors. We provide evidence- and consensus-based policy suggestions to enhance public-access defibrillation and guidance for future research in this area.
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Brooks SC, Clegg GR, Bray J, Deakin CD, Perkins GD, Ringh M, Smith CM, Link MS, Merchant RM, Pezo-Morales J, Parr M, Morrison LJ, Wang TL, Koster RW, Ong MEH. Optimizing outcomes after out-of-hospital cardiac arrest with innovative approaches to public-access defibrillation: A scientific statement from the International Liaison Committee on Resuscitation. Resuscitation 2022; 172:204-228. [PMID: 35181376 DOI: 10.1016/j.resuscitation.2021.11.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Out-of-hospital cardiac arrest is a global public health issue experienced by ≈3.8 million people annually. Only 8% to 12% survive to hospital discharge. Early defibrillation of shockable rhythms is associated with improved survival, but ensuring timely access to defibrillators has been a significant challenge. To date, the development of public-access defibrillation programs, involving the deployment of automated external defibrillators into the public space, has been the main strategy to address this challenge. Public-access defibrillator programs have been associated with improved outcomes for out-of-hospital cardiac arrest; however, the devices are used in <3% of episodes of out-of-hospital cardiac arrest. This scientific statement was commissioned by the International Liaison Committee on Resuscitation with 3 objectives: (1) identify known barriers to public-access defibrillator use and early defibrillation, (2) discuss established and novel strategies to address those barriers, and (3) identify high-priority knowledge gaps for future research to address. The writing group undertook systematic searches of the literature to inform this statement. Innovative strategies were identified that relate to enhanced public outreach, behavior change approaches, optimization of static public-access defibrillator deployment and housing, evolved automated external defibrillator technology and functionality, improved integration of public-access defibrillation with existing emergency dispatch protocols, and exploration of novel automated external defibrillator delivery vectors. We provide evidence- and consensus-based policy suggestions to enhance public-access defibrillation and guidance for future research in this area.
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Hajeb-Mohammadalipour S, Cascella A, Valentine M, Chon KH. Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR. SENSORS (BASEL, SWITZERLAND) 2021; 21:8210. [PMID: 34960308 PMCID: PMC8708115 DOI: 10.3390/s21248210] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 12/01/2021] [Accepted: 12/03/2021] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
| | | | | | - Ki H. Chon
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA;
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Sashidhar D, Kwok H, Coult J, Blackwood J, Kudenchuk PJ, Bhandari S, Rea TD, Kutz JN. Machine learning and feature engineering for predicting pulse presence during chest compressions. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210566. [PMID: 34804564 PMCID: PMC8580432 DOI: 10.1098/rsos.210566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
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.
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Affiliation(s)
- Diya Sashidhar
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
| | - Heemun Kwok
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
- Department of Emergency Medicine, University of Washington, Seattle, WA 98195, USA
| | - Jason Coult
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Jennifer Blackwood
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
| | - Peter J. Kudenchuk
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
- Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Shiv Bhandari
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Thomas D. Rea
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA
- Center for Progress in Resuscitation, University of Washington, Seattle, WA 98195, USA
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Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation. SENSORS 2021; 21:s21124105. [PMID: 34203701 PMCID: PMC8232133 DOI: 10.3390/s21124105] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/07/2021] [Accepted: 06/10/2021] [Indexed: 02/07/2023]
Abstract
High performance of the shock advisory analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important for better management of the resuscitation protocol. It should provide fewer interruptions of chest compressions (CC) for non-shockable organized rhythms (OR) and Asystole, or prompt CC stopping for early treatment of shockable ventricular fibrillation (VF). Major disturbing factors are strong CC artifacts corrupting raw ECG, which we aimed to analyze with optimized end-to-end convolutional neural network (CNN) without pre-filtering or additional sensors. The hyperparameter random search of 1500 CNN models with 2-7 convolutional layers, 5-50 filters and 5-100 kernel sizes was done on large databases from independent OHCA interventions for training (3001 samples) and validation (2528 samples). The best model, named CNN3-CC-ECG network with three convolutional layers (filters@kernels: 5@5,25@20,50@20) presented Sensitivity Se(VF) = 89%(268/301), Specificity Sp(OR) = 91.7%(1504/1640), Sp(Asystole) = 91.1%(3325/3650) on an independent test OHCA database. CNN3-CC-ECG's ability to effectively extract features from raw ECG signals during CPR was comprehensively demonstrated, and the dependency on the CPR corruption level in ECG was tested. We denoted a significant drop of Se(VF) = 74.2% and Sp(OR) = 84.6% in very strong CPR artifacts with a signal-to-noise ratio of SNR < -9 dB, p < 0.05. Otherwise, for strong, moderate and weak CC artifacts (SNR > -9 dB, -6 dB, -3 dB), we observed insignificant performance differences: Se(VF) = 92.5-96.3%, Sp(OR) = 93.4-95.5%, Sp(Asystole) = 92.6-94.0%, p > 0.05. Performance stability with respect to CC rate was validated. Generalizable application of the optimized computationally efficient CNN model was justified by an independent OHCA database, which to our knowledge is the largest test dataset with real-life cardiac arrest rhythms during CPR.
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Isasi I, Irusta U, Aramendi E, Olsen JA, Wik L. Shock decision algorithm for use during load distributing band cardiopulmonary resuscitation. Resuscitation 2021; 165:93-100. [PMID: 34098032 DOI: 10.1016/j.resuscitation.2021.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/18/2021] [Accepted: 05/30/2021] [Indexed: 10/21/2022]
Abstract
AIM Chest compressions delivered by a load distributing band (LDB) induce artefacts in the electrocardiogram. These artefacts alter shock decisions in defibrillators. The aim of this study was to demonstrate the first reliable shock decision algorithm during LDB compressions. METHODS The study dataset comprised 5813 electrocardiogram segments from 896 cardiac arrest patients during LDB compressions. Electrocardiogram segments were annotated by consensus as shockable (1154, 303 patients) or nonshockable (4659, 841 patients). Segments during asystole were used to characterize the LDB artefact and to compare its characteristics to those of manual artefacts from other datasets. LDB artefacts were removed using adaptive filters. A machine learning algorithm was designed for the shock decision after filtering, and its performance was compared to that of a commercial defibrillator's algorithm. RESULTS Median (90% confidence interval) compression frequencies were lower and more stable for the LDB than for the manual artefact, 80 min-1 (79.9-82.9) vs. 104.4 min-1 (48.5-114.0). The amplitude and waveform regularity (Pearson's correlation coefficient) were larger for the LDB artefact, with 5.5 mV (0.8-23.4) vs. 0.5 mV (0.1-2.2) (p < 0.001) and 0.99 (0.78-1.0) vs. 0.88 (0.55-0.98) (p < 0.001). The shock decision accuracy was significantly higher for the machine learning algorithm than for the defibrillator algorithm, with sensitivity/specificity pairs of 92.1/96.8% (machine learning) vs. 91.4/87.1% (defibrillator) (p < 0.001). CONCLUSION Compared to other cardiopulmonary resuscitation artefacts, removing the LDB artefact was challenging due to larger amplitudes and lower compression frequencies. The machine learning algorithm achieved clinically reliable shock decisions during LDB compressions.
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Affiliation(s)
- I Isasi
- Communications Engineering Department, University of the Basque Country UPV/EHU, Plaza Ingeniero Torres Quevedo S/N, 48013 Bilbao, Bizkaia, Spain
| | - U Irusta
- Communications Engineering Department, University of the Basque Country UPV/EHU, Plaza Ingeniero Torres Quevedo S/N, 48013 Bilbao, Bizkaia, Spain; Biocruces Bizkaia Health Research Institute, Cruces Plaza, 48903 Barakaldo, Bizkaia, Spain
| | - E Aramendi
- Communications Engineering Department, University of the Basque Country UPV/EHU, Plaza Ingeniero Torres Quevedo S/N, 48013 Bilbao, Bizkaia, Spain; Biocruces Bizkaia Health Research Institute, Cruces Plaza, 48903 Barakaldo, Bizkaia, Spain
| | - J A Olsen
- National Advisory Unit for Prehospital Emergency Medicine (NAKOS) and Department of Anaesthesiology, Oslo University Hospital and University of Oslo, PO Box 4956 Nydalen, N-0424 Oslo, Norway
| | - L Wik
- National Advisory Unit for Prehospital Emergency Medicine (NAKOS) and Department of Anaesthesiology, Oslo University Hospital and University of Oslo, PO Box 4956 Nydalen, N-0424 Oslo, Norway
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Zuo F, Ding Y, Dai C, Wei L, Gong Y, Wang J, Shen Y, Li Y. Estimating the amplitude spectrum area of ventricular fibrillation during cardiopulmonary resuscitation using only ECG waveform. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:619. [PMID: 33987317 PMCID: PMC8106002 DOI: 10.21037/atm-20-7166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Amplitude spectrum area (AMSA) calculated from ventricular fibrillation (VF) can be used to monitor the effectiveness of chest compression (CC) and optimize the timing of defibrillation. However, reliable AMSA can only be obtained during CC pause because of artifacts. In this study, we sought to develop a method for estimating AMSA during cardiopulmonary resuscitation (CPR) using only the electrocardiogram (ECG) waveform. Methods Intervals of 8 seconds ECG and CC-related references, including 4 seconds during CC and an adjacent 4 seconds without CC, were collected before 1,008 defibrillation shocks from 512 out-of-hospital cardiac arrest patients. Signal quality was analyzed based on the irregularity of autocorrelation of VF. If signal quality index (SQI) was high, AMSA would be calculated from the original signal. Otherwise, CC-related artifacts would be constructed and suppressed using the least mean square filter from VF before calculation of AMSA. The algorithm was optimized using 480 training shocks and evaluated using 528 independent testing shocks. Results Overall, CC resulted in lower SQI [0.15 (0.04-0.61) with CC vs. 0.75 (0.61-0.83) without CC, P<0.01] and higher AMSA [11.2 (7.7-16.2) with CC vs. 7.2 (4.9-10.6) mVHz without CC, P<0.01] values. The predictive accuracy (49.2% vs. 66.5%, P<0.01) and area under the receiver operating characteristic curve (AUC) (0.647 vs. 0.734, P<0.01) were significantly decreased during CC. Using the proposed method, the estimated AMSA was 7.1 (5.0-15.2) mVHz, the predictive accuracy was 67.0% and the AUC was 0.713, which were all comparable with those calculated without CC. Conclusions Using the signal quality-based artifact suppression method, AMSA can be reliably estimated and continuously monitored during CPR.
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Affiliation(s)
- Feng Zuo
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China.,Department of Information Technology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Youde Ding
- Department of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Chenxi Dai
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Liang Wei
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Yushun Gong
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
| | - Juan Wang
- Department of Emergency, Southwest Hospital, Army Medical University, Chongqing, China
| | - Yiming Shen
- Department of Emergency, Chongqing Emergency Medical Center, Chongqing, China
| | - Yongqin Li
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China
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10
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Mohindra R, Cheskes S. Look through and see: Validation of a CPR artifact removal algorithm for AEDs used in OHCA. Resuscitation 2021; 162:415-416. [PMID: 33631290 DOI: 10.1016/j.resuscitation.2021.02.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 02/09/2021] [Indexed: 11/28/2022]
Affiliation(s)
- Rohit Mohindra
- Department of Emergency Medicine, North York General Hospital, Toronto, ON, Canada; Schwartz Reisman Emergency Medicine Institute, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, Toronto, ON, Canada; Faculty of Engineering and Architecture, Ryerson University, Toronto, ON, Canada.
| | - Sheldon Cheskes
- Sunnybrook Center for Prehospital Medicine, Toronto, ON, Canada; Department of Family and Community Medicine, Division of Emergency Medicine University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, Toronto, ON, Canada
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Coult J, Rea TD, Blackwood J, Kudenchuk PJ, Liu C, Kwok H. A method to predict ventricular fibrillation shock outcome during chest compressions. Comput Biol Med 2020; 129:104136. [PMID: 33278632 DOI: 10.1016/j.compbiomed.2020.104136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 11/11/2020] [Accepted: 11/18/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes and could potentially enable a patient-specific, guided approach to resuscitation. However, VF analysis during resuscitation is confounded by cardiopulmonary resuscitation (CPR) artifact in the ECG, challenging continuous application to guide therapy throughout resuscitation. We therefore sought to design a method to predict VF shock outcomes during CPR. METHODS Study data included 4577 5-s VF segments collected during and without CPR prior to defibrillation attempts in N = 1151 arrest patients. Using training data (460 patients), an algorithm was designed to predict the VF shock outcomes of defibrillation success (return of organized ventricular rhythm) and functional survival (Cerebral Performance Category 1-2). The algorithm was designed with variable-frequency notch filters to reduce CPR artifact in the ECG based on real-time chest compression rate. Ten ECG features and three dichotomous patient characteristics were developed to predict outcomes. These variables were combined using support vector machines and logistic regression. Algorithm performance was evaluated by area under the receiver operating characteristic curve (AUC) to predict outcomes in validation data (691 patients). RESULTS AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71-0.77) during CPR and 0.77 (0.74-0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72-0.78) during CPR and 0.76 (0.74-0.79) without CPR. CONCLUSION A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy.
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Affiliation(s)
- Jason Coult
- Department of Medicine, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA.
| | - Thomas D Rea
- Department of Medicine, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Public Health, Seattle & King County, Seattle, WA, USA
| | - Jennifer Blackwood
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Public Health, Seattle & King County, Seattle, WA, USA
| | - Peter J Kudenchuk
- Department of Medicine, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Public Health, Seattle & King County, Seattle, WA, USA
| | - Chenguang Liu
- Philips Emergency Care & Resuscitation, Bothell, WA, USA
| | - Heemun Kwok
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; Department of Emergency Medicine, University of Washington, Seattle, WA, USA
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Coult J, Blackwood J, Rea TD, Kudenchuk PJ, Kwok H. A Method to Detect Presence of Chest Compressions During Resuscitation Using Transthoracic Impedance. IEEE J Biomed Health Inform 2020; 24:768-774. [PMID: 31144648 PMCID: PMC7235095 DOI: 10.1109/jbhi.2019.2918790] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Interruptions in chest compressions during treatment of out-of-hospital cardiac arrest are associated with lower likelihood of successful resuscitation. Real-time automated detection of chest compressions may improve CPR administration during resuscitation, and could facilitate application of next-generation ECG algorithms that employ different parameters depending on compression state. In contrast to accelerometer sensors, transthoracic impedance (TTI) is commonly acquired by defibrillators. We sought to develop and evaluate the performance of a TTI-based algorithm to automatically detect chest compressions. METHODS Five-second TTI segments were collected from patients with out-of-hospital cardiac arrest treated by one of four defibrillator models. Segments with and without chest compressions were collected prior to each of the first four defibrillation shocks (when available) from each case. Patients were divided randomly into 40% training and 60% validation groups. From the training segments, we identified spectral and time-domain features of the TTI associated with compressions. We used logistic regression to predict compression state from these features. Performance was measured by sensitivity and specificity in the validation set. The relationship between performance and TTI segment length was also evaluated. RESULTS The algorithm was trained using 1859 segments from 460 training patients. Validation sensitivity and specificity were >98% using 2727 segments from 691 validation patients. Validation performance was significantly reduced using segments shorter than 3.2 s. CONCLUSIONS A novel method can reliably detect the presence of chest compressions using TTI. These results suggest potential to provide real-time feedback in order to improve CPR performance or facilitate next-generation ECG rhythm algorithms during resuscitation.
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Kwok H, Coult J, Blackwood J, Bhandari S, Kudenchuk P, Rea T. Electrocardiogram-based pulse prediction during cardiopulmonary resuscitation. Resuscitation 2020; 147:104-111. [DOI: 10.1016/j.resuscitation.2019.11.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 11/11/2019] [Accepted: 11/21/2019] [Indexed: 11/27/2022]
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Hu Y, Tang H, Liu C, Jing D, Zhu H, Zhang Y, Yu X, Zhang G, Xu J. The performance of a new shock advisory algorithm to reduce interruptions during CPR. Resuscitation 2019; 143:1-9. [PMID: 31377393 DOI: 10.1016/j.resuscitation.2019.07.026] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 07/01/2019] [Accepted: 07/22/2019] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To explore a new algorithm and strategy for rhythm analysis during chest compressions (CCs), and to improve the efficiency of cardiopulmonary resuscitation (CPR) by minimizing interruptions. METHODS The clinical data and ECG of patients with sudden cardiac arrest (CA) from three hospitals in China were collected with Philips MRx monitor/defibrillators. The length of each analyzed ECG segment was 23 s, the first 11.5 s was selected to contain CPR compressions, the next 5 s had no compressions, and the last 6.5 s had no requirement. Three experienced emergency doctors annotated the ECG segments without compression artifacts. A two-step analysis through CPR (ATC) algorithm was applied to the selected data. The first step was analysis during chest compressions. If a shockable rhythm was not detected, compression-free analysis followed. The results of the ATC algorithm were compared with the annotations by the physicians, to determine the sensitivity and specificity of the algorithm. RESULTS In total 166 CA patients were included with 100 out-of-hospital cardiac arrest (OHCA) patients and 66 in-hospital cardiac arrest (IHCA) patients. A total of 1578 ECG segments were analyzed, including 115 (7.3%) shockable rhythms, 1278 (81.0%) non-shockable rhythms, and 185 (11.7%) intermediate/unknown rhythms. The specificity of all non-shockable rhythms was 99.8% at the end of chest compressions, and 99.5% after analysis without compression artifact. 70.5% of ventricular fibrillation (VF) rhythms were detected by the end of chest compressions. After the CC-free analysis, 93.6% of VF was identified. CONCLUSION The ATC algorithm achieved sensitivity of 93.6% and specificity of 99.5% after the two-step analysis, and 70.5% of the patients with shockable rhythms did not require CC-free analysis. Such an approach has the potential to substantially reduce CC interruptions when identifying shockable rhythms.
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Affiliation(s)
- Yingying Hu
- Department of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China; The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China
| | - Hanqi Tang
- Department of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Chenguang Liu
- Philips Emergency Care & Resuscitation, Bothell, WA, 98012, USA
| | - Daoyuan Jing
- Department of Emergency Medicine, Jinhua Municipal Central Hospital, Zhejiang Province, 321000, China
| | - Huadong Zhu
- Department of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yazhi Zhang
- Department of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Xuezhong Yu
- Department of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Guoxiu Zhang
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China
| | - Jun Xu
- Department of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
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Coult J, Blackwood J, Sherman L, Rea TD, Kudenchuk PJ, Kwok H. Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest. Circ Arrhythm Electrophysiol 2019; 12:e006924. [PMID: 30626208 PMCID: PMC6532650 DOI: 10.1161/circep.118.006924] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Quantitative measures of the ventricular fibrillation (VF) ECG waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation because compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize cardiopulmonary resuscitation interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study's primary outcome. METHODS Five-second VF ECG segments were collected with and without chest compressions before 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and 3 combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ≤2) using 460 training cases, and their performance evaluated using 691 independent test cases. RESULTS Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve ranging from 0.56 to 0.75 (median, 0.73) without chest compressions and from 0.53 to 0.75 (median, 0.69) with compressions ( P<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.73-0.78) and with compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.72-0.78; P=0.75 for difference). CONCLUSIONS VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared with compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest area under the receiver operating characteristic curve. Such machine learning models may, therefore, offer means to guide resuscitation during uninterrupted cardiopulmonary resuscitation.
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Affiliation(s)
- Jason Coult
- Department of Bioengineering, University of Washington,
Seattle, WA
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
| | - Jennifer Blackwood
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- King County Emergency Medical Services, Seattle King County
Department of Public Health, Seattle, WA
| | - Lawrence Sherman
- Department of Bioengineering, University of Washington,
Seattle, WA
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- Department of Medicine, University of Washington School of
Medicine, Seattle, WA
| | - Thomas D. Rea
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- King County Emergency Medical Services, Seattle King County
Department of Public Health, Seattle, WA
- Department of Medicine, University of Washington School of
Medicine, Seattle, WA
| | - Peter J. Kudenchuk
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- King County Emergency Medical Services, Seattle King County
Department of Public Health, Seattle, WA
- Division of Cardiology, University of Washington School of
Medicine, Seattle, WA
| | - Heemun Kwok
- Center for Progress in Resuscitation, University of
Washington, Seattle, WA
- Department of Emergency Medicine, University of Washington
School of Medicine, Seattle, WA
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Ruiz de Gauna S, Leturiondo M, Gutiérrez JJ, Ruiz JM, González-Otero DM, Russell JK, Daya M. Enhancement of capnogram waveform in the presence of chest compression artefact during cardiopulmonary resuscitation. Resuscitation 2018; 133:53-58. [PMID: 30278204 DOI: 10.1016/j.resuscitation.2018.09.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Revised: 09/17/2018] [Accepted: 09/26/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND Current resuscitation guidelines emphasize the use of waveform capnography to help guide rescuers during cardiopulmonary resuscitation (CPR). However, chest compressions often cause oscillations in the capnogram, impeding its reliable interpretation, either visual or automated. The aim of the study was to design an algorithm to enhance waveform capnography by suppressing the chest compression artefact. METHODS Monitor-defibrillator recordings from 202 patients in out-of-hospital cardiac arrest were analysed. Capnograms were classified according to the morphology of the artefact. Ventilations were annotated using the transthoracic impedance signal acquired through defibrillation pads. The suppression algorithm is designed to operate in real-time, locating distorted intervals and restoring the envelope of the capnogram. We evaluated the improvement in automated ventilation detection, estimation of ventilation rate, and detection of excessive ventilation rates (over-ventilation) using the capnograms before and after artefact suppression. RESULTS A total of 44 267 ventilations were annotated. After artefact suppression, sensitivity (Se) and positive predictive value (PPV) of the ventilation detector increased from 91.9/89.5% to 98.0/97.3% in the distorted episodes (83/202). Improvement was most noticeable for high-amplitude artefact, for which Se/PPV raised from 77.6/73.5% to 97.1/96.1%. Estimation of ventilation rate and detection of over-ventilation also upgraded. The suppression algorithm had minimal impact in non-distorted data. CONCLUSION Ventilation detection based on waveform capnography improved after chest compression artefact suppression. Moreover, the algorithm enhances the capnogram tracing, potentially improving its clinical interpretation during CPR. Prospective research in clinical settings is needed to understand the feasibility and utility of the method.
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Affiliation(s)
- Sofía Ruiz de Gauna
- Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain.
| | - Mikel Leturiondo
- Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain
| | - J Julio Gutiérrez
- Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain
| | - Jesus M Ruiz
- Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain
| | - Digna M González-Otero
- Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain
| | - James K Russell
- Department of Emergency Medicine, Oregon Health & Science University, OHSU, 97239-3098 Portland, OR, USA
| | - Mohamud Daya
- Department of Emergency Medicine, Oregon Health & Science University, OHSU, 97239-3098 Portland, OR, USA
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Gutiérrez JJ, Leturiondo M, Ruiz de Gauna S, Ruiz JM, Leturiondo LA, González-Otero DM, Zive D, Russell JK, Daya M. Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform. PLoS One 2018; 13:e0201565. [PMID: 30071008 PMCID: PMC6072040 DOI: 10.1371/journal.pone.0201565] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 07/17/2018] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND During cardiopulmonary resuscitation (CPR), there is a high incidence of capnograms distorted by chest compression artifact. This phenomenon adversely affects the reliability of automated ventilation detection based on the analysis of the capnography waveform. This study explored the feasibility of several filtering techniques for suppressing the artifact to improve the accuracy of ventilation detection. MATERIALS AND METHODS We gathered a database of 232 out-of-hospital cardiac arrest defibrillator recordings containing concurrent capnograms, compression depth and transthoracic impedance signals. Capnograms were classified as non-distorted or distorted by chest compression artifact. All chest compression and ventilation instances were also annotated. Three filtering techniques were explored: a fixed-coefficient (FC) filter, an open-loop (OL) adaptive filter, and a closed-loop (CL) adaptive filter. The improvement in ventilation detection was assessed by comparing the performance of a capnogram-based ventilation detection algorithm with original and filtered capnograms. RESULTS Sensitivity and positive predictive value of the ventilation algorithm improved from 91.9%/89.5% to 97.7%/96.5% (FC filter), 97.6%/96.7% (OL), and 97.0%/97.1% (CL) for the distorted capnograms (42% of the whole set). The highest improvement was obtained for the artifact named type III, for which performance improved from 77.8%/74.5% to values above 95.5%/94.5%. In addition, errors in the measurement of ventilation rate decreased and accuracy in the detection of over-ventilation increased with filtered capnograms. CONCLUSIONS Capnogram-based ventilation detection during CPR was enhanced after suppressing the artifact caused by chest compressions. All filtering approaches performed similarly, so the simplicity of fixed-coefficient filters would take advantage for a practical implementation.
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Affiliation(s)
- Jose Julio Gutiérrez
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain
| | - Mikel Leturiondo
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain
- * E-mail:
| | - Sofía Ruiz de Gauna
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain
| | - Jesus María Ruiz
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain
| | - Luis Alberto Leturiondo
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain
| | - Digna María González-Otero
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain
| | - Dana Zive
- Department of Emergency Medicine, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America
| | - James Knox Russell
- Department of Emergency Medicine, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America
| | - Mohamud Daya
- Department of Emergency Medicine, Oregon Health & Science University (OHSU), Portland, Oregon, United States of America
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Fumagalli F, Silver AE, Tan Q, Zaidi N, Ristagno G. Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology. Heart Rhythm 2018; 15:248-255. [DOI: 10.1016/j.hrthm.2017.09.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Indexed: 11/25/2022]
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19
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Coult J, Kwok H, Sherman L, Blackwood J, Kudenchuk PJ, Rea TD. Ventricular fibrillation waveform measures combined with prior shock outcome predict defibrillation success during cardiopulmonary resuscitation. J Electrocardiol 2017; 51:99-106. [PMID: 28893389 DOI: 10.1016/j.jelectrocard.2017.07.016] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Indexed: 11/26/2022]
Abstract
AIM Amplitude Spectrum Area (AMSA) and Median Slope (MS) are ventricular fibrillation (VF) waveform measures that predict defibrillation shock success. Cardiopulmonary resuscitation (CPR) obscures electrocardiograms and must be paused for analysis. Studies suggest waveform measures better predict subsequent shock success when combined with prior shock success. We determined whether this relationship applies during CPR. METHODS AMSA and MS were calculated from 5-second pre-shock segments with and without CPR, and compared to logistic models combining each measure with prior return of organized rhythm (ROR). RESULTS VF segments from 692 patients were analyzed during CPR before 1372 shocks and without CPR before 1283 shocks. Combining waveform measures with prior ROR increased areas under receiver operating characteristic curves for AMSA/MS with CPR (0.66/0.68 to 0.73/0.74, p<0.001) and without CPR (0.71/0.72 to 0.76/0.76, p<0.001). CONCLUSIONS Prior ROR improves prediction of shock success during CPR, and may enable waveform measure calculation without chest compression pauses.
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Affiliation(s)
- Jason Coult
- Department of Bioengineering, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA.
| | - Heemun Kwok
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA.
| | - Lawrence Sherman
- Department of Bioengineering, University of Washington, Seattle, WA, USA; Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA.
| | - Jennifer Blackwood
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; King County Emergency Medical Services, Seattle King County Department of Public Health, Seattle, WA, USA.
| | - Peter J Kudenchuk
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA; King County Emergency Medical Services, Seattle King County Department of Public Health, Seattle, WA, USA.
| | - Thomas D Rea
- Center for Progress in Resuscitation, University of Washington, Seattle, WA, USA; Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA; King County Emergency Medical Services, Seattle King County Department of Public Health, Seattle, WA, USA.
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Zhang G, Wu T, Wan Z, Song Z, Yu M, Wang D, Li L, Chen F, Xu X. A method to differentiate between ventricular fibrillation and asystole during chest compressions using artifact-corrupted ECG alone. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 141:111-117. [PMID: 28241962 DOI: 10.1016/j.cmpb.2017.01.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 01/29/2017] [Indexed: 06/06/2023]
Abstract
In recent years, numerous adaptive filtering techniques have been developed to suppress the chest compression (CC) artifact for reliable analysis of the electrocardiogram (ECG) rhythm without CC interruption. Unfortunately, the result of rhythm diagnosis during CCs is still unsatisfactory in many studies. The misclassification between corrupted asystole (ASY) and corrupted ventricular fibrillation (VF) is generally regarded as one of the major reasons for the poor performance of reported methods. In order to improve the diagnosis of VF/ASY corrupted by CCs, a novel method combining a least mean-square (LMS) filter and an amplitude spectrum area (AMSA) analysis was developed based only on the analysis of the surface of the corrupted ECG episode. This method was tested on 253 VF and 160 ASY ECG samples from subjects who experienced cardiac arrest using a porcine model and was compared with six other algorithms. The validation results indicated that this method, which yielded a satisfactory result with a sensitivity of 93.3%, a specificity of 96.3% and an accuracy of 94.8%, is superior to the other reported techniques. After improvement using the human ECG records in real cardiopulmonary resuscitation (CPR) scenarios, the algorithm is promising for corrupted VF/ASY detection with no hardware alterations in clinical practice.
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Affiliation(s)
- Guang Zhang
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China
| | - Taihu Wu
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China
| | - Zongming Wan
- Department of Pharmacology, Logistics University of Chinese People's Armed Police Forces, Tianjin, China
| | - Zhenxing Song
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China
| | - Ming Yu
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China
| | - Dan Wang
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China
| | - Liangzhe Li
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China
| | - Feng Chen
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China.
| | - Xinxi Xu
- Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China.
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See through ECG technology during cardiopulmonary resuscitation to analyze rhythm and predict defibrillation outcome. Curr Opin Crit Care 2016; 22:199-205. [DOI: 10.1097/mcc.0000000000000297] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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22
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Gong Y, Gao P, Wei L, Dai C, Zhang L, Li Y. An Enhanced Adaptive Filtering Method for Suppressing Cardiopulmonary Resuscitation Artifact. IEEE Trans Biomed Eng 2016; 64:471-478. [PMID: 27168590 DOI: 10.1109/tbme.2016.2564642] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cardiopulmonary resuscitation (CPR) must be interrupted for reliable rhythm analysis in current automatic external defibrillators because of artifacts produced by chest compressions. However, interruptions in CPR adversely affect the restoration of spontaneous circulation and survival. Suppressing CPR artifacts by digital signal processing techniques is a promising method to enable rhythm analysis during chest compressions, which would eliminate CPR interruptions for rhythm analysis. Although numerous methods have been developed to suppress CPR artifacts, the accuracy of rhythm analysis is still inadequate due to the residual artifact components in the filtered signal. This study proposes an enhanced adaptive filtering method to suppress CPR artifacts. A total of 183 shockable and 453 nonshockable segments of ECG signal, together with CPR-related reference signal, were extracted from 233 out of hospital cardiac arrest patients. The method was optimized on a training set with 85 shockable and 211 nonshockable segments, and evaluated on a testing set with 98 shockable and 242 nonshockable segments. Compared with artifact corrupted ECG signals, the signal-to-noise ratio (SNR) increased from -9.8 ± 12.5 to 11.2 ± 11.8 dB, and the accuracy was improved from 74.1% to 92.0% after filtering with the proposed method. Compared with the traditional adaptive filter, the SNR was improved by 1.7 dB and the accuracy was improved by 5.6 points. These results indicated that the proposed method could effectively suppress the chest compression related artifacts and improve the accuracy of rhythm analysis during uninterrupted CPR.
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Removal of cardiopulmonary resuscitation artifacts with an enhanced adaptive filtering method: an experimental trial. BIOMED RESEARCH INTERNATIONAL 2014; 2014:140438. [PMID: 24795878 PMCID: PMC3985144 DOI: 10.1155/2014/140438] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Revised: 02/25/2014] [Accepted: 02/26/2014] [Indexed: 11/18/2022]
Abstract
Current automated external defibrillators mandate interruptions of chest compression to avoid the effect of artifacts produced by CPR for reliable rhythm analyses. But even seconds of interruption of chest compression during CPR adversely affects the rate of restoration of spontaneous circulation and survival. Numerous digital signal processing techniques have been developed to remove the artifacts or interpret the corrupted ECG with promising result, but the performance is still inadequate, especially for nonshockable rhythms. In the present study, we suppressed the CPR artifacts with an enhanced adaptive filtering method. The performance of the method was evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering the CPR corrupted ECG signals. The dataset comprised 283 segments of shockable and 280 segments of nonshockable ECG signals during CPR recorded from 22 adult pigs that experienced prolonged cardiac arrest. For the unfiltered signals, the sensitivity and specificity were 99.3% and 46.8%, respectively. After filtering, a sensitivity of 93.3% and a specificity of 96.0% were achieved. This animal trial demonstrated that the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR.
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Rhythm analysis during cardiopulmonary resuscitation: past, present, and future. BIOMED RESEARCH INTERNATIONAL 2014; 2014:386010. [PMID: 24527445 PMCID: PMC3910663 DOI: 10.1155/2014/386010] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2013] [Accepted: 12/09/2013] [Indexed: 11/18/2022]
Abstract
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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