1
|
Jaureguibeitia X, Aramendi E, Irusta U, Alonso E, Aufderheide TP, Schmicker RH, Hansen M, Suchting R, Carlson JN, Idris AH, Wang HE. Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets. Resuscitation 2021; 168:44-51. [PMID: 34509553 DOI: 10.1016/j.resuscitation.2021.09.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services. AIM To introduce and evaluate a methodology to automatically extract cardiopulmonary resuscitation (CPR) quality data in a uniform and systematic way from OHCA datasets from multiple heterogeneous sources. METHODS A dataset of 2236 OHCA cases from multiple defibrillator models and manufacturers was analyzed. Chest compressions were automatically identified using the thoracic impedance and compression depth signals. Device event time-stamps and clinical annotations were used to set the start and end of the analysis interval, and to identify periods with spontaneous circulation. A manual audit of the automatic annotations was conducted and used as gold standard. Chest compression fraction (CCF), rate (CCR) and interruption ratio were computed as CPR quality variables. The unsigned error between the automated procedure and the gold standard was calculated. RESULTS Full-episode median errors below 2% in CCF, 1 min-1 in CCR, and 1.5% in interruption ratio, were measured for all signals and devices. The proportion of cases with large errors (>10% in CCF and interruption ratio, and >10 min-1 in CCR) was below 10%. Errors were lower for shorter sub-intervals of interest, like the airway insertion interval. CONCLUSIONS An automated methodology was validated to accurately compute CPR metrics in large and heterogeneous OHCA datasets. Automated processing of defibrillator files and the associated clinical annotations enables the aggregation and analysis of CPR data from multiple sources.
Collapse
Affiliation(s)
- Xabier Jaureguibeitia
- Communications Engineering Department, University of the Basque Country UPV/EHU, Bilbao, Spain
| | - Elisabete Aramendi
- Communications Engineering Department, University of the Basque Country UPV/EHU, Bilbao, Spain; Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.
| | - Unai Irusta
- Communications Engineering Department, University of the Basque Country UPV/EHU, Bilbao, Spain; Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | - Erik Alonso
- Department of Applied Mathematics, University of the Basque Country UPV/EHU, Bilbao, Spain
| | - Tom P Aufderheide
- Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Robert H Schmicker
- Clinical Trial Center, Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Matthew Hansen
- Department of Emergency Medicine, Oregon Health and Science University, Portland, OR, United States
| | - Robert Suchting
- Department of Psychiatry and Behavioral, Sciences University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Jestin N Carlson
- Department of Emergency Medicine, Saint Vincent Hospital, Allegheny Health Network, Erie, PA, United States; Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ahamed H Idris
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Henry E Wang
- Department of Emergency Medicine, Ohio State University, Columbus, OH, United States
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Isasi I, Irusta U, Aramendi E, Idris AH, Sörnmo L. Restoration of the electrocardiogram during mechanical cardiopulmonary resuscitation. Physiol Meas 2020; 41:105006. [DOI: 10.1088/1361-6579/ab9e53] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
5
|
Isasi I, Irusta U, Elola A, Aramendi E, Eftestol T, Kramer-Johansen J, Wik L. A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1903-1907. [PMID: 31946270 DOI: 10.1109/embc.2019.8856784] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of defibrillators inaccurate. There is evidence that methods consisting of adaptive filters that remove the CPR artifact followed by machine learning (ML) based algorithms are able to make reliable shock/no-shock decisions during compressions. However, there is room for improvement in the performance of these methods. The objective was to design a robust ML framework for a reliable shock/no-shock decision during CPR. The study dataset contained 596 shockable and 1697 nonshockable ECG segments obtained from 273 cases of out-of-hospital cardiac arrest. Shock/no-shock labels were adjudicated by expert reviewers using ECG intervals without artifacts. First, CPR artifacts were removed from the ECG using a Least Mean Squares (LMS) filter. Then, 38 shock/no-shock decision features based on the Stationary Wavelet Transform (SWT) were extracted from the filtered ECG. A wapper-based feature selection method was applied to select the 6 best features for classification. Finally, 4 state-of-the-art ML classifiers were tested to make the shock/no-shock decision. These diagnoses were compared with the rhythm annotations to compute the Sensitivity (Se) and Specificity (Sp). All classifiers achieved an Se above 94.5%, Sp above 95.5% and an accuracy around 96.0%. They all exceeded the 90% Se and 95% Sp minimum values recommended by the American Heart Association.
Collapse
|
6
|
Jaureguibeitia X, Irusta U, Aramendi E, Owens PC, Wang HE, Idris AH. Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation. IEEE J Biomed Health Inform 2020; 24:2580-2588. [PMID: 31976918 DOI: 10.1109/jbhi.2020.2967643] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are concurrently delivered by a mechanical CPR device. A total of 423 OHCA patients treated with mechanical CPR were included, 761 analysis intervals were selected which in total comprised 5 884 minutes and contained 34 864 ventilations. Ground truth ventilations were determined using the expired CO 2 channel. The method uses adaptive signal processing to obtain the impedance ventilation waveform. Then, 14 features were calculated from the ventilation waveform and fed to a random forest (RF) classifier to discriminate false positive detections from actual ventilations. The RF feature importance was used to determine the best feature subset for the classifier. The method was trained and tested using stratified 10-fold cross validation (CV) partitions. The training/test process was repeated 20 times to statistically characterize the results. The best ventilation detector had a median (interdecile range, IDR) F 1-score of 96.32 (96.26-96.37). When used to provide feedback in 1-min intervals, the median (IDR) error and relative error in ventilation rate were 0.002 (-0.334-0.572) min-1 and 0.05 (-3.71-9.08)%, respectively. An accurate ventilation detector during mechanical CPR was demonstrated. The algorithm could be introduced in current equipment for feedback on ventilation rate and quality, and it could contribute to improve OHCA survival rates.
Collapse
|
7
|
Isasi I, Irusta U, Elola A, Aramendi E, Ayala U, Alonso E, Kramer-Johansen J, Eftestol T. A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions. IEEE Trans Biomed Eng 2018; 66:1752-1760. [PMID: 30387719 DOI: 10.1109/tbme.2018.2878910] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally demanding, and their accuracy could be improved. The objective of this work was to introduce a computationally efficient algorithm for shock decision during piston-driven CPR with increased accuracy. METHODS The study dataset contains 201 shockable and 844 nonshockable ECG segments from 230 cardiac arrest patients treated with the LUCAS-2 mechanical CPR device. Compression artifacts were removed using the state-of-the-art adaptive filters, and shock/no-shock discrimination features were extracted from the stationary wavelet transform analysis of the filtered ECG, and fed to a support vector machine (SVM) classifier. Quasi-stratified patient wise nested cross-validation was used for feature selection and SVM hyperparameter optimization. The procedure was repeated 50 times to statistically characterize the results. RESULTS Best results were obtained for a six-feature classifier with mean (standard deviation) sensitivity, specificity, and total accuracy of 97.5 (0.4), 98.2 (0.4), and 98.1 (0.3), respectively. The algorithm presented a five-fold reduction in computational demands when compared to the best available methods, while improving their balanced accuracy by 3 points. CONCLUSIONS The accuracy of the best available methods was improved while drastically reducing the computational demands. SIGNIFICANCE An efficient and accurate method for shock decisions during mechanical CPR is now available to improve therapy and contribute to increase cardiac arrest survival.
Collapse
|
8
|
Isasi I, Irusta U, Aramendi E, Ayala U, Alonso E, Kramer-Johansen J, Eftestol T. A Multistage Algorithm for ECG Rhythm Analysis During Piston-Driven Mechanical Chest Compressions. IEEE Trans Biomed Eng 2018; 66:263-272. [PMID: 29993407 DOI: 10.1109/tbme.2018.2827304] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL An accurate rhythm analysis during cardiopulmonary resuscitation (CPR) would contribute to increase the survival from out-of-hospital cardiac arrest. Piston-driven mechanical compression devices are frequently used to deliver CPR. The objective of this paper was to design a method to accurately diagnose the rhythm during compressions delivered by a piston-driven device. METHODS Data was gathered from 230 out-of-hospital cardiac arrest patients treated with the LUCAS 2 mechanical CPR device. The dataset comprised 201 shockable and 844 nonshockable ECG segments, whereof 270 were asystole (AS) and 574 organized rhythm (OR). A multistage algorithm (MSA) was designed, which included two artifact filters based on a recursive least squares algorithm, a rhythm analysis algorithm from a commercial defibrillator, and an ECG-slope-based rhythm classifier. Data was partitioned randomly and patient-wise into training (60%) and test (40%) for optimization and validation, and statistically meaningful results were obtained repeating the process 500 times. RESULTS The mean (standard deviation) sensitivity (SE) for shockable rhythms, specificity (SP) for nonshockable rhythms, and the total accuracy of the MSA solution were: 91.7 (6.0), 98.1 (1.1), and 96.9 (0.9), respectively. The SP for AS and OR were 98.0 (1.7) and 98.1 (1.4), respectively. CONCLUSIONS The SE/SP were above the 90%/95% values recommended by the American Heart Association for shockable and nonshockable rhythms other than sinus rhythm, respectively. SIGNIFICANCE It is possible to accurately diagnose the rhythm during mechanical chest compressions and the results considerably improve those obtained by previous algorithms.
Collapse
|
9
|
Variation and correlation of end–tidal CO2 and amplitude spectrum area in a refractory ventricular fibrillation. A case from the ReCaPTa study. Resuscitation 2018; 122:e19-e20. [DOI: 10.1016/j.resuscitation.2017.11.049] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 11/19/2017] [Indexed: 11/19/2022]
|
10
|
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.
Collapse
|