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.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque Government through the grant PRE_2018_2_0260

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de Economía, Industria y Competitividad, Gobierno de España (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section

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.This work was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades through Grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), by the Basque Government through Grant IT1229-19 and Grant PRE2020_1_0177, and by the university of the Basque Country (UPV/EHU) under Grant COLAB20/01

Machine learning and signal processing contributions to identify circulation states during out-of-hospital cardiac arrest

212 p. (eusk) 216 p. (eng.)Bat-bateko bihotz geldialdia (BBG) ustekabeko bihotz jardueraren etenaldi gisa definitzen da [9], non odol perfusioa ez baita iristenez burmuinera, ez beste ezinbesteko organoetara. BBGa ahalik eta azkarren tratatu behar da berpizte terapien bidez bat-bateko bihotz heriotza (BBH) ekiditeko [10, 11]. Ohikoena BBGa ospitalez kanpoko inguruneetan gertatzea da [12] eta kasu gehienetan ez da lekukorik egoten [13]. Horregatik, berpizte terapien aplikazio goiztiarra erronka mediku eta soziala da gaur egun

A Comparison of Patient History- and EKG-based Cardiac Risk Scores.

Patient-specific risk scores are used to identify individuals at elevated risk for cardiovascular disease. Typically, risk scores are based on patient habits and medical history - age, sex, race, smoking behavior, and prior vital signs and diagnoses. We explore an alternative source of information, a patient's raw electrocardiogram recording, and develop a score of patient risk for various outcomes. We compare models that predict adverse cardiac outcomes following an emergency department visit, and show that a learned representation (e.g. deep neural network) of raw EKG waveforms can improve prediction over traditional risk factors. Further, we show that a simple model based on segmented heart beats performs as well or better than a complex convolutional network recently shown to reliably automate arrhythmia detection in EKGs. We analyze a large cohort of emergency department patients and show evidence that EKG-derived scores can be more robust to patient heterogeneity

Prediction and monitoring of in-hospital cardiac arrest

Background: In-hospital cardiac arrest (IHCA) is a global health concern of major importance, associated with a poor prognosis. IHCA is frequently heralded by a deterioration of vital signs, and many cases are considered preventable. Hence, prevention has become a key strategy. The overall aim of this thesis was to study the prevention of IHCA, by means of prediction and monitoring, with a view to improve patient safety. Methods: Study I and III are observational cohort studies, based on the Swedish Registry of Cardiopulmonary Resuscitation (SRCR). In study III, we also collected additional data from medical records in a small, hypothesis-generating group of patients. Study II and IV are prospective, observational cohort studies based on patients reviewed by Rapid Response Teams (RRTs) in 26 and 24 Swedish hospitals, respectively. In study IV, additional data on long-term survival was obtained from either medical records or the personal information directory, containing population registration data. Specific study aims and results: In study I, we investigated how 30-day survival after IHCA was influenced by ECG monitoring at the time of collapse, as well as clinical factors that determined whether patients were ECG monitored adjacent to cardiac arrest (CA). In all, 24,790 patients were enrolled in the SRCR between 2008 and 2017. After applying the exclusion criteria, 19,225 patients remained, of which 52% were monitored at the time of collapse. In all, 30-day survival was 30%. ECG monitoring at the time of CA was associated with a Hazard Ratio of 0.62 (95% Confidence Interval 0.60-0.64) for 30-day mortality. The strongest predictor of ECG monitoring adjacent to IHCA was location in hospital. There were tangible variations in the frequency of patients who were ECG monitored at the time of collapse between Swedish regions and across hospitals. In study II, we investigated the predictive power of NEWS 2, as compared to NEWS, in identifying patients at risk of Serious Adverse Events (SAEs) within 24 hours of an RRT-review. In all, 1,065 patients, reviewed by RRTs in general wards during the study period between October 2019 and January 2020, were included. After applying the exclusion criteria, 898 patients were eligible for complete case analyses. In all, 37% of the patients were admitted to the Intensive care unit (ICU) within 24 hours of RRT-review. In-hospital mortality and IHCA were uncommon (6% and 1% respectively). The Area Under the Receiver Operating Characteristic (AUROC) for both NEWS and NEWS 2 was 0.62 for the composite outcome, and 0.69/0.67 for mortality. Regarding the outcome unanticipated ICU admission, the AUROC was 0.59 and 0.60, respectively, while the AUROC for IHCA was 0.51 (NEWS) and 0.47 (NEWS 2), respectively. In study III, we investigated 30-day survival and ROSC in patients suffering from IHCA, who were reviewed by an RRT within 24 hours prior to the CA, as compared to those without such review. Furthermore, we studied patient centred factors prior to RRT activation, the timeliness of the RRT-review as well as the reason for the RRT-review. We also investigated the association between RRT interventions and outcome. During the study period between 2014 and 2021, 19,973 patents were enrolled in the SRCR. After applying the exclusion criteria, 12,915 patients remained. Among these IHCA patients, there was an RRT/ICU contact within 24 hours prior to the CA in 2,058 cases (19%). The adjusted 30-day survival was lower among patients reviewed by an RRT prior to IHCA (25% vs. 33%, p <0.001). Regarding ROSC, we did not observe any difference between the groups. The propensity score based Odds Ratio for 30- day survival was 0.92 for patients who were reviewed by an RRT (95% CI 0.90 to 0.94, p <0.001), as compared to those who were not RRT- reviewed within 24 hours prior to IHCA. A respiratory cause of CA was more common among IHCA patients who were reviewed by an RRT. In the small, explorative subgroup (n=82), 24% of the RRT activations were delayed, and respiratory distress was the most common RRT trigger. We observed a significantly lower 30-day survival among patients triaged to remain at ward compared to those triaged to a higher level of care (2% vs. 20%, p 0.016). In study IV, we explored the impact of age on the ability of NEWS 2 to predict IHCA, unanticipated ICU-admission, or death, and the composite of these three SAEs, within 24 hours of review by an RRT. Furthermore, we aimed to investigate 30-, 90- and 180-day mortality, and the discriminative ability of NEWS 2 in the prediction of long-term mortality among RRT-reviewed patients. In this multi-centre study based on data prospectively collected by RRTs, the NEWS 2 scores of all patients were retrospectively, digitally calculated by the study team. Age was analysed as a continuous variable, in a spline regression model, and categorized into five different models, subsequently explored as additive variables to NEWS 2. The discriminative ability of NEWS 2 in predicting 30-day mortality improved by adding age as a covariate (from AUROC 0.66, 0.62-0.70 to 0.70, 0.65-0.73, p=0.01). There were differences across age groups, with the best predictive performance identified among patients aged 45-54 years. The 30-, 90-and 180-day mortality was 31%, 33%, and 36%, respectively. Conclusion: ECG monitoring at the time of IHCA was associated with a 38% reduction of adjusted mortality. Despite this finding, only one in two IHCA patients were ECG monitored. The most important factor influencing ECG monitoring was which type of hospital ward the patient was admitted to. The tangible variations in the frequency of ECG monitoring adjacent to IHCA observed between Swedish regions and across hospitals need to be investigated in future studies. Guidelines for the monitoring of patients at risk of CA could contribute to an improved outcome. The prognostic accuracy of NEWS 2 in predicting mortality within 24 hours of an RRT-review was acceptable, whereas the discriminative ability in prediction of unanticipated ICU-admission and the composite outcome was rather weak. Regarding the prediction of IHCA, NEWS 2 performed poorly. There was no difference in the prognostic accuracy between NEWS and NEWS 2; however, the discriminative ability was not considered sufficient to serve as a triage tool in RRT-reviewed patients. In-hospital cardiac arrest among patients who were reviewed by an RRT prior to CA was associated with a poorer prognosis, and a more frequent respiratory aetiology of the CA. In the explorative sub-group of patients, RRT activation was frequently delayed, the most common trigger for RRT-review was respiratory distress, and escalation of the level of care was associated with an improved prognosis. Early identification of patients with abnormal respiratory vital signs, followed by a timely response, may have a potential to improve the prognosis for patients reviewed by an RRT and prevent IHCA. Adding age as a covariate improved the discriminative ability of NEWS 2 in the prediction of 30-day mortality among RRT-reviewed patients. The ability differed across age categories. Overall, the long-term prognosis of RRT-reviewed patients was poor. Our results indicate that age merits further validation as a covariate to improve the performance of NEWS 2

ESC core curriculum for the general cardiologist (2013)

[No abstract available