Aim To develop and evaluate a method to detect circulation in the presence of organized rhythms (ORs) during resuscitation using signals acquired by defibrillation pads. Methods Segments containing electrocardiogram (ECG) and thoracic impedance (TI) signals free of artifacts were used. The ECG corresponded to ORs classified as pulseless electrical acitivity (PEA) or pulse-generating rhythm (PR). A first dataset containing 1091 segments was split into training and test sets to develop and validate the circulation detector. The method processed ECG and TI to obtain the impedance circulation component (ICC). Morphological features were extracted from ECG and ICC, and combined into a classifier to discriminate between PEA and PR. The performance of the method was evaluated in terms of sensitivity (PR) and specificity (PEA). A second dataset (86 segments from different patients) was used to assess two application of the method: confirmation of arrest by recognizing absence of circulation during ORs and detection of return of spontaneous circulation (ROSC) during resuscitation. In both cases, time to confirmation of arrest/ROSC was determined. Results The method showed a sensitivity/specificity of 92.1%/90.3% and 92.2%/91.9% for training and test sets respectively. The method confirmed cardiac arrest with a specificity of 93.3% with a median delay of 0 s after the first OR annotation. ROSC was detected with a sensitivity of 94.4% with a median delay of 57 s from ROSC onset. Conclusion The method showed good performance, and can be reliably used to distinguish perfusing from non-perfusing ORs.
Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 µV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.
Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.
Automated External Defibrillators (AED) incorporate a shock decision algorithm that analyzes the patient's electrocardiogram (EKG), allowing lay persons to provide life saving defibrillation therapy to out-of-hospital cardiac arrest (OHCA) patients. The most accurate shock decision algorithms are based on deep learning, but these algorithms have not been trained and tested using OHCA data. In this study we propose novel deep learning architectures for shock decision algorithms based on convolutional and residual networks. EKG electronic recordings from a cohort of 852 OHCA cases (4216 AED EKG analyses) were used in the study. EKGs were annotated by a pool of six expert clinicians resulting in 3718 nonshockable and 498 shockable EKGs. Data were partitioned patient wise in a stratified way to train and test the models using 10-fold cross validation, and the procedure was repeated 100 times for statistical evaluation. Performance was assessed using sensitivity (shockable), specificity (non-shockable) and accuracy, and the analysis was conducted for EKG segments of decreasing duration. The best model had median (interdecile range) accuracies of 98.6 (98.5 -98.7)%, 98.4 (98.2 -98.6)%, 98.2 (97.9 -98.4)%, and 97.6 (97.4 -97.8)%, for 4, 3, 2 and 1 second EKG segments, respectively. The minimum 90% sensitivity and 95% specificity requirements established by the American Heart Association for shock decision algorithms were met, and the best model presented significantly greater accuracy (p<0.05 McNemar test) than previous deep learning solutions for all segment durations. Moreover, the first AHA compliant shock decision algorithm using 1-s segments was demonstrated. This should contribute to a combined optimization of defibrillation and cardiopulmonary resuscitation therapy to improve OHCA survival. INDEX TERMS automated external defibrillator (AED), electrocardiogram (EKG), convolutional neural networks (CNN), deep learning, ventricular fibrillation (VF), residual networks. I. INTRODUCTION 1 C ARDIAC arrest is the unexpected sudden cessation 2 of the cardiac function, and occurs mostly in a 3 pre-hospital setting. Out-of-hospital cardiac arrest (OHCA) 4 constitutes a major global health problem. Only in the US 5 one thousand OHCA events are estimated to occur daily, 6 with survival rates around 10% [1]. Two therapies are key for OHCA survival: defibrillation, to restore the normal 8 function of the heart; and cardiopulmonary resuscitation 9
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