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of the match rates comparing the manually and automatically derived variable values. (a) shows match rates for rhythms at the preselected times from the database. (b) shows data regarding the defibrillator logs of shock data. (c) shows the times for ECG start, shock times, first compression and last compression times, VF onset times, and time of return of spontaneous circulation. All results are given in percentages of the ratio: correct/(correct + error). For the time variables, the lower row shows the ratio of automatically generated codes for missing values matching the manually given missing codes (gray bars).
Source publication
Background:
During resuscitation of cardiac arrest victims a variety of information in electronic format is recorded as part of the documentation of the patient care contact and in order to be provided for case review for quality improvement. Such review requires considerable effort and resources. There is also the problem of interobserver effects...
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Zusammenfassung
Studienziel
Ziele sind die Verlaufsanalyse und der Vergleich mit ausschließlich manuell reanimierten Patienten sowie die Erfassung der Einflussfaktoren bei Patienten, bei denen die mechanische Thoraxkompressionshilfe Lund University Cardiac Assist System (LUCAS2 TM ) als Add-on-Therapie am Notarzteinsatzfahrzeug (NEF) Innsbruck ver...
Background-—Little is known about whether cardiopulmonary resuscitation (CPR) training can increase bystander CPR in the community or the appropriate target number of CPR trainings. Herein, we aimed to demonstrate community-wide aggressive dissemination of CPR training and evaluate temporal trends in bystander CPR.
Methods and Results-—We provided...
Objective: To determine the success rates of adult cardiopulmonary resuscitation (CPR) and identify the predictors of successful CPR at the emergency room (ER) at a university-based hospital. Materials and Methods: Adult patients that experienced cardiac arrest and received CPR at the ER were prospectively observed. The primary outcomes were the ra...
BACKGROUND
Three million people in Sweden are trained in cardiopulmonary resuscitation (CPR). Whether this training increases the frequency of bystander CPR or the survival rate among persons who have out-of-hospital cardiac arrests has been questioned.
METHODS
We analyzed a total of 30,381 out-of-hospital cardiac arrests witnessed in Sweden from J...
Introduction
Annually, approximately 4% of the entire adult population of Denmark participate in certified basic life support (BLS) courses. It is still unknown whether increases in BLS course participation in a geographical area increase bystander cardiopulmonary resuscitation (CPR) or survival from out-of-hospital cardiac arrest (OHCA). The aim o...
Citations
... Defibrillator files should also be considered collected and integrated with cardiac arrest registries, which will allow automatic registration of information that is entered manually today. 8,71 The integration of defibrillator files into the resuscitation registries would also allow the release of the further potential of AI based models requiring larger data sets. This will allow for analysis and statistical modelling on much larger data sets and allow for comparison of results from different EMS systems and hospitals. ...
Background
A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO2, which allows for research on how patients demonstrate different responses to CPR. The aim of this review is to give an overview of methodological challenges and opportunities in using defibrillator data for research.
Methods
The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen’s linear model, Weibull regression and joint models.
Conclusions
The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.
... The annotation of cardiac rhythms in full-length resuscitation episodes would contribute to a 28 richer retrospective analysis of resuscitation data and to a better understanding of the interplay 29 between therapy and patient response. 1 It could help to determine optimal chest compression 30 strategies, a better understanding of the effects of chest compression pauses and their duration, or to 31 maximize the likelihood of successful defibrillation attempts. [2][3][4][5][6][7] To date, cardiac rhythm classification 32 and the identification of rhythm transitions with and without chest compression artefacts have been 33 done manually by expert clinicians. ...
... Furthermore, we demonstrate and evaluate the accuracy of the system on 57 a comprehensive dataset of clinically annotated complete resuscitation episodes. This architecture 58 integrates a body of knowledge developed over the last decade in signal processing applied to 59 4 resuscitation data annotation, in line with the general annotation framework proposed by Eftestøl et 60 al. 1 rates above 120 bpm were annotated as VT. 91 Finally, data were reviewed by an independent biomedical engineer, and intervals with severe 92 noise, large artefacts (not due to compressions), or with loss of ECG signal were labelled as uncertain 93 and discarded from further analysis. ...
Aim:
An automatic resuscitation rhythm annotator (ARA) would facilitate and enhance retrospective analysis of resuscitation data, contributing to a better understanding of the interplay between therapy and patient response. The objective of this study was to define, implement, and demonstrate an ARA architecture for complete resuscitation episodes, including chest compression pauses (CC-pauses) and chest compression intervals (CC-intervals).
Methods:
We analyzed 126.5h of ECG and accelerometer-based chest-compression depth data from 281 out-of-hospital cardiac arrest (OHCA) patients. Data were annotated by expert reviewers into asystole (AS), pulseless electrical activity (PEA), pulse-generating rhythm (PR), ventricular fibrillation (VF), and ventricular tachycardia (VT). Clinical pulse annotations were based on patient-charts and impedance measurements. An ARA was developed for CC-pauses, and was used in combination with a chest compression artefact removal filter during CC-intervals. The performance of the ARA was assessed in terms of the unweighted mean of sensitivities (UMS).
Results:
The UMS of the ARA were 75.0% during CC-pauses and 52.5% during CC-intervals, 55-points and 32.5-points over a random guess (20% for five categories). Filtering increased the UMS during CC-intervals by 5.2-points. Sensitivities for AS, PEA, PR, VF, and VT were 66.8%, 55.8%, 86.5%, 82.1% and 83.8% during CC-pauses; and 51.1%, 34.1%, 58.7%, 86.4%, and 32.1% during CC-intervals.
Conclusions:
A general ARA architecture was defined and demonstrated on a comprehensive OHCA dataset. Results showed that semi-automatic resuscitation rhythm annotation, which may involve further revision/correction by clinicians for quality assurance, is feasible. The performance (UMS) dropped significantly during CC-intervals and sensitivity was lowest for PEA.
... [1][2][3][4][5][6][7] Manual rhythm annotation is a time consuming task and an obstacle for handling large datasets efficiently. Eftestøl et al. 8 demonstrated recently how rhythm state and chest compression sequence annotations could be used as a basis from which higher-level review parameters can be derived automatically. In addition, Ayala et al. 9 demonstrated automatic techniques to determine chest compressions, and Irusta et al. 10 proposed an algorithms for cardiopulmonary resuscitation (CPR) artifact removal. ...
... Future developments may then include the combined analysis of the ECG, impedance data, and/or ETCO 2 to discriminate PEA from PR; 18,19 or, in the context of a fully automated review system, to combine the analyses of the algorithms with clinical ROSC annotations made on site. 8 Inspection of the data in Table 2, Figs. 2 and 3 reveals the main sources of misclassification. Errors are due to borderline rhythms such as: VF with low amplitude and dominant frequency (AS/VF discrimination), bradycardic rhythms (AS/PEA) and tachycardias of either ventricular or supraventricular origin (VT/PEA and VT/PR). ...
... These issues have been identified as knowledge gaps in resuscitation science by the International Liaison Committee on Resuscitation. 27 Eftestøl et al. 8 previously demonstrated how a large number of parameters registered during the review of resuscitation episodes could be automatically replicated from a minimal set of basic parameters describing the resuscitation episode. These basic parameters included start and end times of chest compressions, the electronic defibrillator event report, and annotations of the rhythm transition times. ...
Aim:
Resuscitation guidelines recommend different treatments depending on the patient's cardiac rhythm. Rhythm interpretation is a key tool to retrospectively evaluate and improve the quality of treatment. Manual rhythm annotation is time consuming and an obstacle for handling large resuscitation datasets efficiently. The objective of this study was to develop a system for automatic rhythm interpretation by using signal processing and machine learning algorithms.
Methods:
Data from 302 out of hospital cardiac arrest patients were used. In total 1669 3-second artefact free ECG segments with clinical rhythm annotations were extracted. The proposed algorithms combine 32 features obtained from both wavelet- and time-domain representations of the ECG, followed by a feature selection procedure based on the wrapper method in a nested cross-validation architecture. Linear and quadratic discriminant analyses (LDA and QDA) were used to automatically classify the segments into one of five rhythm types: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse generating rhythms (PR).
Results:
The overall accuracy for the best algorithm was 68%. VT, VF, and AS are recognized with sensitivities of 71, 75, and 79%, respectively. Sensitivities for PEA and PR were 55 and 56%, respectively, which reflects the difficulty of identifying pulse using only the ECG.
Conclusions:
An ECG based automatic rhythm interpreter for resuscitation has been demonstrated. The interpreter handles VT, VF and AS well, while PEA and PR discrimination poses a more difficult problem.
Methods:
The dataset consisted of 1631 3- second ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. 47 wavelet and time domain based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied.
Results:
The best results were obtained for ANN classifier with Bayesian regularization back propagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively.
Conclusions:
The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low.
Significance:
We have made an important step towards making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.
In order to monitor the cardiac arrest patients response to therapy, there is a need for methods that can reliably interpret the different types of cardiac rhythms that can occur during a resuscitation episode. These rhythms can be categorized to five groups; ventricular tachycardia, ventricular fibrillation, pulseless electrical activity, asystole, and pulse generating rhythm. The objective of this study was to develop machine learning algorithms to automatically recognize these rhythms. We proposed a detection algorithm based on the nearest-manifold classification approach using a group of 8 time-domain features as statistical measures on the signal itself, as well as the first and second differences. The overall accuracy of the cardiac arrest rhythm interpretation is 79% which is 9% better than our prior work. The sensitivity/specificity of shockable/non-shockable rhythms is 92/95%.
