Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

Abstract Background Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2), using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA) technique. Methods A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM) model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold. Results The integrative model performs real-time, short-term (7.8 second) analysis of the Electrocardiogram (ECG). For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64.6% and 60.9%, respectively. Conclusion We report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.http://deepblue.lib.umich.edu/bitstream/2027.42/112730/1/12911_2012_Article_558.pd

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes

Objective The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals. Materials and Methods Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique. Results 358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent \u3e 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity. Conclusion At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model

ASSESSMENT AND PREDICTION OF CARDIOVASCULAR STATUS DURING CARDIAC ARREST THROUGH MACHINE LEARNING AND DYNAMICAL TIME-SERIES ANALYSIS

In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viability for decision-support in clinical settings. The approach, which focuses on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic signals. Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. For a given desired sensitivity, the proposed model provides a significantly higher accuracy and specificity as compared to the state-of-the-art. Notably, within the range of 80-90% of sensitivity, the method provides about 40% higher specificity. This means that when trained to have the same level of sensitivity, the model will yield far fewer false positives (unnecessary shocks). Also introduced is a new model that predicts recurrence of arrest after a successful countershock is delivered. To date, no other work has sought to build such a model. I validate the method by reporting multiple performance metrics calculated on (blind) test sets

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

[EN] 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.This work received financial support from Spanish Ministerio de Economia y Competitividad and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), projects TEC2015-64678-R and DPI2017-83952-C3; from UPV/EHU through the grant PIF15/190 and through project GIU17/031; from the Basque Government through grant PRE-2016-1-0012; and from Junta de Comunidades de Castilla-La Mancha through SBPLY/17/180501/000411.Chicote, B.; Irusta, U.; Aramendi, E.; Alcaraz, R.; Rieta, JJ.; Isasi, I.; Alonso, D.... (2018). 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ECG waveform dataset for predicting defibrillation outcome in out-of-hospital cardiac arrested patients

The provided database of 260 ECG signals was collected from patients with out-of-hospital cardiac arrest while treated by the emergency medical services. Each ECG signal contains a 9 second waveform showing ventricular fibrillation, followed by 1 min of post-shock waveform. Patients’ ECGs are made available in multiple formats. All ECGs recorded during the prehospital treatment are provided in PFD files, after being anonymized, printed in paper, and scanned. For each ECG, the dataset also includes the whole digitized waveform (9 s pre- and 1 min post-shock each) and numerous features in temporal and frequency domain extracted from the 9 s episode immediately prior to the first defibrillation shock. Based on the shock outcome, each ECG file has been annotated by three expert cardiologists, - using majority decision -, as successful (56 cases), unsuccessful (195 cases), or indeterminable (9 cases). The code for preprocessing, for feature extraction, and for limiting the investigation to different temporal intervals before the shock is also provided. These data could be reused to design algorithms to predict shock outcome based on ventricular fibrillation analysis, with the goal to optimize the defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation and/or drug administration) for enhancing resuscitation. © 202

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

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.This work received financial support from Spanish Ministerio de Economia y Competitividad, projects TEC2013-31928 and TEC2014-52250-R, and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), project TEC2015-64678-R; from Junta de Comunidades de Castilla La Mancha, project PPII-2014-026-P; and from UPV/EHU through the grant PIF15/190 and through its research unit UFI11/16.Chicote, B.; Irusta, U.; Alcaraz, R.; Rieta, JJ.; Aramendi, E.; Isasi, I.; Alonso, D.... (2016). Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy. 18(9):1-17. https://doi.org/10.3390/e18090313S11718

Системы поддержки принятия решений в хирургии

ХИРУРГИЯРЕШЕНИЯ КЛИНИЧЕСКИЕ, ИНФОРМАЦИОННЫЕ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯКОМПЬЮТЕРНЫЕ СИСТЕМ

A State Space Odyssey — The Multiplex Dynamics of Cardiac Arrhythmias

With three million people worldwide (three hundred thousand people in the United States alone) experiencing sudden cardiac arrest per year, it is one of the most common causes of death in developed countries. Ventricular fibrillation, a dysfunction of the heart characterized by a highly chaotic spatio-temporal wave dynamics, is the main cause for sudden cardiac arrest. The application of a high-energy defibrillation shock, as the current medical treatment to restore the sinus rhythm, comes along with severe side-effects, among others additional damage of the heart. Furthermore, patients with an ICD (implantable cardioverter-defibrillator) in particular suffer from posttraumatic stress symptoms. The goal of this thesis is to investigate the dynamics of the heart (and in particular the nature of cardiac arrhythmias (specifically ventricular fibrillation)) using concepts and perceptions from the dynamical systems theory. On the basis of the interdisciplinary interplay between mathematical approaches and interaction with experimental and clinical knowledge and results, two general scientific objectives are addressed: Derive an enhanced understanding of the dynamics during episodes of ventricular fibrillation, including the development of concepts for the improvement of current defibrillation techniques and suggestions for completely new strategies which may find their way into the clinical application. Obtain novel insights into the fundamental dynamics of complex, nonlinear systems (thus excitable systems and beyond). These objectives are addressed using numerical simulations, which constitute the main tool to investigate specific research questions. The results of this thesis are organized in four chapters, each focusing on one specific question: The first results chapter is dealing with the mechanism of spontaneous termination of ventricular fibrillation. We investigate the transient behavior of spiral and scroll wave dynamics using different cell models. The observed transients can be classified into the group of so called type-II supertransients. We find, that in 3D simulations, a critical thickness of the medium plays an essential role. Basic features of the simulations agree with general observations of clinicians, e.g. that larger heart muscle volumes increase the risk of cardiac arrhythmias. In the second results chapter, we address the question whether a self-termination of a chaotic episode can be predicted. By applying small but finite perturbations to specific trajectories of chaotic spiral wave dynamics we find that the state space structure close to the “exits” of the chaotic regime changes significantly. We could verify this effect also in low-dimensional maps. This analysis shows, that although the upcoming self-termination is not visible in conventional variables, it should in principle be possible to derive such a quantity. In the third results chapter, we investigate complexity fluctuations of the chaotic spatio-temporal dynamics in simulations using realistic heart geometries. We show, that the level of organization of the spatio-temporal dynamics can be estimated by analyzing the time series of a multi-electrode setup. In the last results chapter, we discuss whether a successful termination of chaotic spiral wave dynamics is possible using a minimal interaction with the system. We show, that since the underlying topological object which determines the chaotic dynamics is a chaotic saddle, one can terminate the dynamics (as a proof of concept) by the application of a specific but very small perturbation. We hope that the insights provided by this thesis contribute to the general understanding of cardiac arrhythmias and the nonlinear dynamics of complex systems. The results suggest that an improved medical treatment of cardiac arrhythmias can benefit from: A more detailed state analysis of the dynamics during spatio-temporal chaos, incorporating diverse measure techniques (e.g. multiple-ECG measurements, CT scans, MRI scans). An intervention strategy which should adapt to individual patients and the respective dynamical state of the heart. A variety of new experimental approaches will be available which may help to achieve these goals and to improve the understanding of the phenomena investigated in this thesis: Filament identification in the bulk tissue during experiments using sophisticated ultra sound techniques, inverse ECG measurements for the reconstruction of spatio-temporal wave dynamics or using techniques from optogenetics for the stimulation of cardiac tissue via light pulses are promising candidates which can have a significant impact on the field of cardiac dynamics. This technological progress in combination with novel data analysis techniques from the fields of machine learning or data assimilation and sophisticated simulations of the complex dynamics has great potential to develop advanced and efficient strategies for a patient specific medical treatment