A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest.

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20-30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest.

A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision-recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks.

Chest compressions during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may provoque inaccurate rhythm classification by the algorithm of the defibrillator. The objective of this study was to design an algorithm to produce reliable shock/no-shock decisions during CPR using convolutional neural networks (CNN). A total of 3319 ECG segments of 9 s extracted during chest compressions were used, whereof 586 were shockable and 2733 nonshockable. Chest compression artifacts were removed using a Recursive Least Squares (RLS) filter, and the filtered ECG was fed to a CNN classifier with three convolutional blocks and two fully connected layers for the shock/no-shock classification. A 5-fold cross validation architecture was adopted to train/test the algorithm, and the proccess was repeated 100 times to statistically characterize the performance. The proposed architecture was compared to the most accurate algorithms that include handcrafted ECG features and a random forest classifier (baseline model). The median (90% confidence interval) sensitivity, specificity, accuracy and balanced accuracy of the method were 95.8% (94.6-96.8), 96.1% (95.8-96.5), 96.1% (95.7-96.4) and 96.0% (95.5-96.5), respectively. The proposed algorithm outperformed the baseline model by 0.6-points in accuracy. This new approach shows the potential of deep learning methods to provide reliable diagnosis of the cardiac rhythm without interrupting chest compression therapy.

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest.

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.

Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest.

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.

Direct evaluation of the effect of filtering the chest compression artifacts on the uninterrupted cardiopulmonary resuscitation time.

OBJECTIVES: Filtering the cardiopulmonary resuscitation (CPR) artifact has been a major approach to minimizing interruptions to CPR for rhythm analysis. However, the effects of these filters on interruptions to CPR have not been evaluated. This study presents the first methodology for directly quantifying the effects of filtering on the uninterrupted CPR time. METHODS: A total of 241 shockable and 634 nonshockable out-of-hospital cardiac arrest records (median duration, 150 seconds) from 248 patients were analyzed. Filtering and rhythm analysis were commenced after 1 minute of CPR, and the end point for CPR was established at the time of the first shock diagnosis. Kaplan-Meier curves were used to compute the probability of interrupting CPR as a function of time. The probabilities of delivering 2 minutes of uninterrupted CPR for the shockable and nonshockable rhythms were compared with the 2-minute cycles of uninterrupted CPR recommended by the guidelines. RESULTS: For the nonshockable rhythms, the probabilities of delivering at least 2 and 3 minutes of uninterrupted CPR were 58% (95% confidence interval, 54%-62%) and 48% (44%-52%), respectively. These are the probabilities of reducing and substantially reducing the frequency of CPR interruptions for rhythm analysis. For the shockable rhythms, the probability of avoiding unnecessary CPR prolongation beyond 2 minutes was 100% (99%-100%). CONCLUSIONS: Filtering reduces the frequency of CPR interruptions for rhythm analysis in less than 60% of nonshockable rhythms. New strategies to increase the probability of prolonging CPR for nonshockable rhythms should be defined and evaluated using the methodology proposed in this study.

Physiological effects of providing supplemental air for avalanche victims. A randomised trial.

BACKGROUND: Survival from avalanche burial is dependent on time to extraction, breathing ability, air pocket oxygen content, and avoiding rebreathing of carbon dioxide (CO2). Mortality from asphyxia increases rapidly after burial. Rescue services often arrive too late. Our objective was to evaluate the physiological effects of providing personal air supply in a simulated avalanche scenario as a possible concept to delay asphyxia. We hypothesize that supplemental air toward victim's face into the air pocket will prolong the window of potential survival. METHODS: A prospective randomized crossover experimental field study enrolled 20 healthy subjects in Hemsedal, Norway in March 2019. Subjects underwent in randomized order two sessions (receiving 2 litres per minute of air in front of mouth/nose into the air pocket or no air) in a simulated avalanche scenario with extensive monitoring serving as their own control. RESULTS: A significant increase comparing Control vs Intervention were documented for minimum and maximum end-tidal CO2 (EtCO2), respiration rate, tidal volume, minute ventilation, heart rate, invasive arterial blood pressures, but lower peripheral and cerebral oximetry. Controls compared to Intervention group subjects had a lower study completion rate (26% vs 74%), and minutes in the air pocket before interruption (13.1 ± 8.1 vs 22.4 ± 5.6 vs), respectively. CONCLUSIONS: Participants subject to simulated avalanche burial can maintain physiologic parameters within normal levels for a significantly longer period if they receive supplemental air in front of their mouth/nose into the air pocket. This may extend the time for potential rescue and lead to increased survival.

Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets.

Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.

Airway strategy and ventilation rates in the pragmatic airway resuscitation trial.

BACKGROUND: We sought to describe ventilation rates during out-of-hospital cardiac arrest (OHCA) resuscitation and their associations with airway management strategy and outcomes. METHODS: We analyzed continuous end-tidal carbon dioxide capnography data from adult OHCA enrolled in the Pragmatic Airway Resuscitation Trial (PART). Using automated signal processing techniques, we determined continuous ventilation rates for consecutive 10-second epochs after airway insertion. We defined hypoventilation as a ventilation rate < 6 breaths/min. We defined hyperventilation as a ventilation rate > 12 breaths/min. We compared differences in total and percentage post-airway hyper- and hypoventilation between airway interventions (laryngeal tube (LT) vs. endotracheal intubation (ETI)). We also determined associations between hypo-/hyperventilation and OHCA outcomes (ROSC, 72-hour survival, hospital survival, hospital survival with favorable neurologic status). RESULTS: Adequate post-airway capnography were available for 1,010 (LT n = 714, ETI n = 296) of 3,004 patients. Median ventilation rates were: LT 8.0 (IQR 6.5-9.6) breaths/min, ETI 7.9 (6.5-9.7) breaths/min. Total duration and percentage of post-airway time with hypoventilation were similar between LT and ETI: median 1.8 vs. 1.7 minutes, p = 0.94; median 10.5% vs. 11.5%, p = 0.60. Total duration and percentage of post-airway time with hyperventilation were similar between LT and ETI: median 0.4 vs. 0.4 minutes, p = 0.91; median 2.1% vs. 1.9%, p = 0.99. Hypo- and hyperventilation exhibited limited associations with OHCA outcomes. CONCLUSION: In the PART Trial, EMS personnel delivered post-airway ventilations at rates satisfying international guidelines, with only limited hypo- or hyperventilation. Hypo- and hyperventilation durations did not differ between airway management strategy and exhibited uncertain associations with OCHA outcomes.

Multimodal Algorithms for the Classification of Circulation States During Out-of-Hospital Cardiac Arrest.

GOAL: Identifying the circulation state during out-of-hospital cardiac arrest (OHCA) is essential to determine what life-saving therapies to apply. Currently algorithms discriminate circulation (pulsed rhythms, PR) from no circulation (pulseless electrical activity, PEA), but PEA can be classified into true (TPEA) and pseudo (PPEA) depending on cardiac contractility. This study introduces multi-class algorithms to automatically determine circulation states during OHCA using the signals available in defibrillators. METHODS: A cohort of 60 OHCA cases were used to extract a dataset of 2506 5-s segments, labeled as PR (1463), PPEA (364) and TPEA (679) using the invasive blood pressure, experimentally recorded through a radial/femoral cannulation. A multimodal algorithm using features obtained from the electrocardiogram, the thoracic impedance and the capnogram was designed. A random forest model was trained to discriminate three (TPEA/PPEA/PR) and two (PEA/PR) circulation states. The models were evaluated using repeated patient-wise 5-fold cross-validation, with the unweighted mean of sensitivities (UMS) and F 1-score as performance metrics. RESULTS: The best model for 3-class had a median (interquartile range, IQR) UMS and F 1 of 69.0% (68.0-70.1) and 61.7% (61.0-62.5), respectively. The best two class classifier had median (IQR) UMS and F 1 of 83.9% (82.9-84.5) and 76.2% (75.0-76.9), outperforming all previous proposals in over 3-points in UMS. CONCLUSIONS: The first multiclass OHCA circulation state classifier was demonstrated. The method improved previous algorithms for binary pulse/no-pulse decisions. SIGNIFICANCE: Automatic multiclass circulation state classification during OHCA could contribute to improve cardiac arrest therapy and improve survival rates.

Novel application of thoracic impedance to characterize ventilations during cardiopulmonary resuscitation in the pragmatic airway resuscitation trial.

BACKGROUND: Significant challenges exist in measuring ventilation quality during out-of-hospital cardiopulmonary arrest (OHCA) outcomes. Since ventilation is associated with outcomes in cardiac arrest, tools that objectively describe ventilation dynamics are needed. We sought to characterize thoracic impedance (TI) oscillations associated with ventilation waveforms in the Pragmatic Airway Resuscitation Trial (PART). METHODS: We analyzed CPR process files collected from adult OHCA enrolled in PART. We limited the analysis to cases with simultaneous capnography ventilation recordings at the Dallas-Fort Worth site. We identified ventilation waveforms in the thoracic impedance signal by applying automated signal processing with adaptive filtering techniques to remove overlying artifacts from chest compressions. We correlated detected ventilations with the end-tidal capnography signals. We determined the amplitudes (Ai, Ae) and durations (Di, De) of both insufflation and exhalation phases. We compared differences between laryngeal tube (LT) and endotracheal intubation (ETI) airway management during mechanical or manual chest compressions using Mann-Whitney U-test. RESULTS: We included 303 CPR process cases in the analysis; 209 manual (77 ETI, 132 LT), 94 mechanical (41 ETI, 53 LT). Ventilation Ai and Ae were higher for ETI than LT in both manual (ETI: Ai 0.71 Ω, Ae 0.70 Ω vs LT: Ai 0.46 Ω, Ae 0.45 Ω; p < 0.01 respectively) and mechanical chest compressions (ETI: Ai 1.22 Ω, Ae 1.14 Ω VS LT: Ai 0.74 Ω, Ae 0.68 Ω; p < 0.01 respectively). Ventilations per minute, duration of TI amplitude insufflation and exhalation did not differ among groups. CONCLUSION: Compared with LT, ETI thoracic impedance ventilation insufflation and exhalation amplitude were higher while duration did not differ. TI may provide a novel approach to characterizing ventilation during OHCA.

End-tidal carbon dioxide (ETCO2) and ventricular fibrillation amplitude spectral area (AMSA) for shock outcome prediction in out-of-hospital cardiac arrest. Are they two sides of the same coin?

AIM: Ventricular fibrillation amplitude spectral area (AMSA) and end-tidal carbon dioxide (ETCO2) are predictors of shock success, understood as restoration of an organized rhythm, and return of spontaneous circulation (ROSC). However, little is known about their combined use. We aimed to assess the prediction accuracy when combined, and to clarify if they are correlated in out of hospital cardiac arrest' victims. MATERIALS AND METHODS: Records acquired by external defibrillators in out-of-hospital cardiac arrest patients of the Lombardia Cardiac Arrest registry were processed. The 1-min pre-shock ETCO2 median value (METCO2) was computed from the capnogram and AMSA (2-48 mV.Hz range) computed applying the Fast Fourier Transform to a 2-second pre-shock filtered ECG interval (0.5-30 Hz). Support Vector Machine (SVM) predictive models based on METCO2, AMSA and their combination were fit; results were given as the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. RESULTS: We considered 112 patients with 391 shocks delivered. METCO2 and AMSA were predictors of shock success [AUC (IQR) of the ROC curve: 0.59 (0.56-0.62); 0.68 (0.65-0.72), respectively] and of ROSC [0.56 (0.53-0.59); 0.74 (0.71-0.78),]. Their combination in a SVM model increased the accuracy for predicting shock success [AUC (IQR) of the ROC curve: 0.71 (0.68-0.75)] and ROSC [0.77 (0.73-0.8)]. AMSA and METCO2 were significantly correlated only in patients who achieved ROSC (rho = 0.33 p = 0.03). CONCLUSIONS: AMSA and ETCO2 predict shock success and ROSC after every shock, and their predictive power increases if combined. Notably, they were correlated only in patients who achieved ROSC.

Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets.

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.

Airway strategy and chest compression quality in the Pragmatic Airway Resuscitation Trial.

BACKGROUND: Chest compression (CC) quality is associated with improved out-of-hospital cardiopulmonary arrest (OHCA) outcomes. Airway management efforts may adversely influence CC quality. We sought to compare the effects of initial laryngeal tube (LT) and initial endotracheal intubation (ETI) airway management strategies upon chest compression fraction (CCF), rate and interruptions in the Pragmatic Airway Resuscitation Trial (PART). METHODS: We analyzed CPR process files collected from adult OHCA enrolled in PART. We used automated signal processing techniques and a graphical user interface to calculate CC quality measures and defined interruptions as pauses in chest compressions longer than 3 s. We determined CC fraction, rate and interruptions (number and total duration) for the entire resuscitation and compared differences between LT and ETI using t-tests. We repeated the analysis stratified by time before, during and after airway insertion as well as by successive 3-min time segments. We also compared CC quality between single vs. multiple airway insertion attempts, as well as between bag-valve-mask (BVM-only) vs. ETI or LT. RESULTS: Of 3004 patients enrolled in PART, CPR process data were available for 1996 (1001 LT, 995 ETI). Mean CPR analysis duration were: LT 22.6 ±â€¯10.8 min vs. ETI 25.3 ±â€¯11.3 min (p < 0.001). Mean CC fraction (LT 88% vs. ETI 87%, p = 0.05) and rate (LT 114 vs. ETI 114 compressions per minute (cpm), p = 0.59) were similar between LT and ETI. Median number of CC interruptions were: LT 11 vs. ETI 12 (p = 0.001). Total CC interruption duration was lower for LT than ETI (LT 160 vs. ETI 181 s, p = 0.002); this difference was larger before airway insertion (LT 56 vs. ETI 78 s, p < 0.001). There were no differences in CC quality when stratified by 3-min time epochs. CONCLUSION: In the PART trial, compared with ETI, LT was associated with shorter total CC interruption duration but not other CC quality measures. CC quality may be associated with OHCA airway management.

Restoration of the electrocardiogram during mechanical cardiopulmonary resuscitation.

OBJECTIVE: An artefact-free electrocardiogram (ECG) is essential during cardiac arrest to decide therapy such as defibrillation. Mechanical cardiopulmonary resuscitation (CPR) devices cause movement artefacts that alter the ECG. This study analyzes the effectiveness of mechanical CPR artefact suppression filters to restore clinically relevant ECG information. APPROACH: In total, 495 10 s ECGs were used, of which 165 were in ventricular fibrillation (VF), 165 in organized rhythms (OR) and 165 contained mechanical CPR artefacts recorded during asystole. CPR artefacts and rhythms were mixed at controlled signal-to-noise ratios (SNRs), ranging from -20 dB to 10 dB. Mechanical artefacts were removed using least mean squares (LMS), recursive least squares (RLS) and Kalman filters. Performance was evaluated by comparing the clean and the restored ECGs in terms of restored SNR, correlation-based similarity measures, and clinically relevant features: QRS detection performance for OR, and dominant frequency, mean amplitude and waveform irregularity for VF. For each filter, a shock/no-shock support vector machine algorithm based on multiresolution analysis of the restored ECG was designed, and evaluated in terms of sensitivity (Se) and specificity (Sp). MAIN RESULTS: The RLS filter produced the largest correlation coefficient (0.80), the largest average increase in SNR (9.5 dB), and the best QRS detection performance. The LMS filter best restored VF with errors of 10.3% in dominant frequency, 18.1% in amplitude and 11.8% in waveform irregularity. The Se/Sp of the diagnosis of the restored ECG were 95.1/94.5% using the RLS filter and 97.0/91.4% using the LMS filter. SIGNIFICANCE: Suitable filter configurations to restore ECG waveforms during mechanical CPR have been determined, allowing reliable clinical decisions without interrupting mechanical CPR therapy.

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

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.

Factors affecting the course of resuscitation from cardiac arrest with pulseless electrical activity in children and adolescents.

BACKGROUND: Although in-hospital pediatric cardiac arrests and cardiopulmonary resuscitation occur >15,000/year in the US, few studies have assessed which factors affect the course of resuscitation in these patients. We investigated transitions from Pulseless Electrical Activity (PEA) to Ventricular Fibrillation/pulseless Ventricular Tachycardia (VF/pVT), Return of Spontaneous Circulation (ROSC) and recurrences from ROSC to PEA in children and adolescents with in-hospital cardiac arrest. METHODS: Episodes of cardiac arrest at the Children's Hospital of Philadelphia were prospectively registered. Defibrillators that recorded chest compression depth/rate and ventilation rate were applied. CPR variables, patient characteristics and etiology, and dynamic factors (e.g. the proportion of time spent in PEA or ROSC) were entered as time-varying covariates for the transition intensities under study. RESULTS: In 67 episodes of CPR in 59 patients (median age 15 years) with cardiac arrest, there were 52 transitions from PEA to ROSC, 22 transitions from PEA to VF/pVT, and 23 recurrences of PEA from ROSC. Except for a nearly significant effect of mean compression depth beyond a threshold of 5.7 cm, only dynamic factors that evolved during CPR favored a transition from PEA to ROSC. The latter included a lower proportion of PEA over the last 5 min and a higher proportion of ROSC over the last 5 min. Factors associated with PEA to VF/pVT development were age, weight, the proportion spent in VF/pVT or PEA the last 5 min, and the general transition intensity, while PEA recurrence from ROSC only depended on the general transition intensity. CONCLUSION: The clinical course during pediatric cardiac arrest was mainly influenced by dynamic factors associated with time in PEA and ROSC. Transitions from PEA to ROSC seemed to be favored by deeper compressions.

Impedance Based Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Monitoring ventilation rate is key to improve the quality of cardiopulmonary resuscitation (CPR) and increase the probability of survival in the event of an out-of-hospital cardiac arrest (OHCA). Ventilations produce discernible fluctuations in the thoracic impedance signal recorded by defibrillators. Impedance-based detection of ventilations during CPR is challenging due to chest compression artifacts. This study presents a method for an accurate detection of ventilations when chest compressions are delivered using a piston-driven mechanical device. Data from 223 OHCA patients were analyzed and 399 analysis segments totaling 3101 minutes of mechanical CPR were extracted. A total of 18327 ventilations were annotated using concurrent capnogram recordings. An adaptive least mean squares filter was used to remove compression artifacts. Potential ventilations were detected using a greedy peak detector, and the ventilation waveform was characterized using 8 waveform features. These features were used in a logistic regression classifier to discriminate true ventilations from false positives produced by the greedy peak detector. The classifier was trained and tested using patient wise 10-fold cross validation (CV), and 100 random CV partitions were created to statistically characterize the performance metrics. The peak detector presented a sensitivity (Se) of 99.30%, but a positive predictive value (PPV) of 54.43%. The best classifier configuration used 6 features and improved the mean (sd) Se and PPV of the detector to 93.20% (0.06) and 94.43% (0.04), respectively. When used to measure per minute ventilation rates for feedback to the rescuer, the mean (sd) absolute error in ventilation rate was 0.61 (1.64) min-1. The first impedance-based method to accurately detect ventilations and give feedback on ventilation rate during mechanical CPR has been demonstrated.

Convolutional Recurrent Neural Networks to Characterize the Circulation Component in the Thoracic Impedance during Out-of-Hospital Cardiac Arrest.

Pulse detection during out-of-hospital cardiac arrest remains challenging for both novel and expert rescuers because current methods are inaccurate and time-consuming. There is still a need to develop automatic methods for pulse detection, where the most challenging scenario is the discrimination between pulsed rhythms (PR, pulse) and pulseless electrical activity (PEA, no pulse). Thoracic impedance (TI) acquired through defibrillation pads has been proven useful for detecting pulse as it shows small fluctuations with every heart beat. In this study we analyse the use of deep learning techniques to detect pulse using only the TI signal. The proposed neural network, composed by convolutional and recurrent layers, outperformed state of the art methods, and achieved a balanced accuracy of 90% for segments as short as 3 s.

Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest.

BACKGROUND AND AIM: Unsuccessful defibrillation shocks adversely affect survival from out-of-hospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-of-choice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO2 (EtCO2) could improve the prediction. The aim of this study was to evaluate EtCO2 as predictor of shock success, both individually and in combination with VF-waveform analysis. MATERIALS AND METHODS: In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO2 (MEtCO2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks. RESULTS: MEtCO2 in first shocks was significantly higher for successful than for unsuccessful shocks (31mmHg/25mmHg, p<0.05), but differences were not significant for all shocks (32mmHg/29mmHg, p>0.05). MEtCO2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively. CONCLUSIONS: MEtCO2 predicted defibrillation success only for first shocks. Adding MEtCO2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform features and MEtCO2 were automatically calculated from the device files, so these methods could be introduced in current defibrillators adding only new software.