Feedback systems for the quality of chest compressions during cardiopulmonary resuscitation

167 p.Se define la parada cardiorrespiratoria como la detención súbita de la actividad mecánica del corazón, confirmada por la ausencia de signos de circulación. En caso de parada cardiorrespiratoria, dos actuaciones son clave para la supervivencia del paciente: la reanimación cardiopulmonar (RCP) precoz, y la desfibrilación precoz. La RCP consiste en proporcionar compresiones torácicas y ventilaciones al paciente para mantener un mínimo flujo de sangre oxigenada a los órganos vitales. La calidad de las compresiones está relacionada con la supervivencia del paciente. Por esta razón las guías de resucitación recomiendan el uso de sistemas de feedback que monitorizan la calidad de la RCP en tiempo real. Estos dispositivos se sitúan generalmente entre el pecho del paciente y las manos del rescatador, y guían al rescatador para ayudarle a alcanzar la profundidad y frecuencia de compresión objetivo. Esta tesis explora nuevas alternativas para monitorizar la calidad de las compresiones durante la RCP. Se han seguido dos estrategias: usar la señal de impedancia transtorácica (ITT), que es adquirida por los desfibriladores actuales a través de los parches de desfibrilación, y usar la aceleración del pecho, que podría ser registrada usando un dispositivo adicional

Feasibility of waveform capnography as a non-invasive monitoring tool during cardiopulmonary resuscitation

178 p.Sudden cardiac arrest (SCA) is one of the leading causes of death in the industrialized world and it includes the sudden cessation of circulation and consciousness, confirmed by the absence of pulse and breathing. Cardiopulmonary resuscitation (CPR) is one of the key interventions for patient survival after SCA, a life-saving procedure that combines chest compressions and ventilations to maintain a minimal oxygenated blood flow.To deliver oxygen, an adequate blood flow must be generated, by effective CPR, during the majority of the cardiac arrest time. Although monitoring the quality of CPR performed by rescuers during cardiac arrest has been a huge step forward in resuscitation science, in 2013, a consensus statement from the American Heart Association prioritized a new type of CPR quality monitoring focused on the physiological response of the patient instead of how the rescuer is doing.To that end, current resuscitation guidelines emphasize the use of waveform capnography during CPR for patient monitoring. Among several advantages such as ensure correct tube placement, one of its most important roles is to monitor ventilation rate, helping to avoid potentially harmful over-ventilation. In addition, waveform capnography would enable monitoring CPR quality, early detection of ROSC and determining patient prognosis. However, several studies have reported the appearance of fast oscillations superimposed on the capnogram, hereinafter CC-artifact, which may hinder a feasible use of waveform capnography during CPR. In addition to the possible lack of reliability, several factors need to be taken into account when interpreting ETCO2 measurements. Chest compressions and ventilation have opposing effects on ETCO2 levels. Chest compressions increase CO2 concentration, delivering CO2 from the tissues to the lungs, whilst ventilations remove CO2 from the lungs, decreasing ETCO2. Thus, ventilation rate acts as a significant confounding factor.This thesis analyzes the feasibility of waveform capnography as non-invasive monitoring tool of the physiological response of the patient to resuscitation efforts. A set of four intermediate goals was defined.First, we analyzed the incidence and morphology of the CC-artifact and assessed its negative influence in the detection of ventilations and in ventilation rate and ETCO2 measurement. Second, several artifact suppression techniques were used to improve ventilation detection and to enhance capnography waveform. Third, we applied a novel strategy to model the impact of ventilations and ventilation rate on the exhaled CO2 measured in out-of-hospital cardiac arrest capnograms, which could allow to measure the change in ETCO2 attributable to chest compressions by removing the influence of concurrent ventilations. Finally, we studied if the assessment of the ETCO2 trends during chest compressions pauses could allow to detect return of spontaneous circulation, a metric that could be useful as an adjunct to other decision tool

Feasibility of waveform capnography as a non-invasive monitoring tool during cardiopulmonary resuscitation

178 p.Sudden cardiac arrest (SCA) is one of the leading causes of death in the industrialized world and it includes the sudden cessation of circulation and consciousness, confirmed by the absence of pulse and breathing. Cardiopulmonary resuscitation (CPR) is one of the key interventions for patient survival after SCA, a life-saving procedure that combines chest compressions and ventilations to maintain a minimal oxygenated blood flow.To deliver oxygen, an adequate blood flow must be generated, by effective CPR, during the majority of the cardiac arrest time. Although monitoring the quality of CPR performed by rescuers during cardiac arrest has been a huge step forward in resuscitation science, in 2013, a consensus statement from the American Heart Association prioritized a new type of CPR quality monitoring focused on the physiological response of the patient instead of how the rescuer is doing.To that end, current resuscitation guidelines emphasize the use of waveform capnography during CPR for patient monitoring. Among several advantages such as ensure correct tube placement, one of its most important roles is to monitor ventilation rate, helping to avoid potentially harmful over-ventilation. In addition, waveform capnography would enable monitoring CPR quality, early detection of ROSC and determining patient prognosis. However, several studies have reported the appearance of fast oscillations superimposed on the capnogram, hereinafter CC-artifact, which may hinder a feasible use of waveform capnography during CPR. In addition to the possible lack of reliability, several factors need to be taken into account when interpreting ETCO2 measurements. Chest compressions and ventilation have opposing effects on ETCO2 levels. Chest compressions increase CO2 concentration, delivering CO2 from the tissues to the lungs, whilst ventilations remove CO2 from the lungs, decreasing ETCO2. Thus, ventilation rate acts as a significant confounding factor.This thesis analyzes the feasibility of waveform capnography as non-invasive monitoring tool of the physiological response of the patient to resuscitation efforts. A set of four intermediate goals was defined.First, we analyzed the incidence and morphology of the CC-artifact and assessed its negative influence in the detection of ventilations and in ventilation rate and ETCO2 measurement. Second, several artifact suppression techniques were used to improve ventilation detection and to enhance capnography waveform. Third, we applied a novel strategy to model the impact of ventilations and ventilation rate on the exhaled CO2 measured in out-of-hospital cardiac arrest capnograms, which could allow to measure the change in ETCO2 attributable to chest compressions by removing the influence of concurrent ventilations. Finally, we studied if the assessment of the ETCO2 trends during chest compressions pauses could allow to detect return of spontaneous circulation, a metric that could be useful as an adjunct to other decision tool

Audiovisual Feedback Devices for Chest Compression Quality during CPR

During cardiopulmonary resuscitation (CPR), chest compression quality is the key for patient survival. However, several studies have shown that both professionals and laypeople often apply CPR at improper rates and depths. The use of real-time feedback devices increases adherence to CPR quality guidelines. This chapter explores new alternatives to provide feedback on the quality of chest compressions during CPR. First, we describe and evaluate three methods to compute chest compression depth and rate using exclusively the chest acceleration. To evaluate the accuracy of the methods, we used episodes of simulated cardiac arrest acquired in a manikin model. One of the methods, based on the spectral analysis of the acceleration, was particularly accurate in a wide range of conditions. Then, we assessed the feasibility of using the transthoracic impedance (TI) signal acquired through defibrillation pads to provide feedback on chest compression depth and rate. For that purpose, we retrospectively analyzed three databases of out-of-hospital cardiac arrest episodes. When a wide variety of patients and rescuers were included, TI could not be used to reliably estimate the compression depth. However, compression rate could be accurately estimated. Development of simpler methods to provide feedback on CPR quality could contribute to the widespread of these devices

Reporting and improving quality of cardiopulmonary resuscitation (CPR) during out of hospital cardiac arrest.

Cand.med Jo Kramer-Johansen (f.1969) has studied how quality of CPR can be measured and modified by automated feedback during out of hospital cardiac arrest. The results from 284 episodes of cardiac arrest treated in the ambulance services of Akershus, London, and Stockholm show variable and poor quality of CPR characterized by too shallow chest compressions and too many and too long pauses. In the thesis he discusses and recommends standards for measuring and reporting CPR quality for the purposes of avoiding confounded clinical trials and for quality assurance and improvement in all professional services. The Norwegian Air Ambulance Foundation supported this work with a full time scholarship. Supervisors have been Professor Petter Andreas Steen and Lars Wik (NAKOS)

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

Seinale prozesaketan eta ikasketa automatikoan oinarritutako ekarpenak bihotz-erritmoen analisirako bihotz-biriketako berpiztean

Tesis inglés 218 p. -- Tesis euskera 220 p.Out-of-hospital cardiac arrest (OHCA ) is characterized by the sudden loss of the cardiac function, andcauses around 10% of the total mortality in developed countries. Survival from OHCA depends largelyon two factors: early defibrillation and early cardiopulmonary resuscitation (CPR). The electrical shock isdelivered using a shock advice algorithm (SAA) implemented in defibrillators. Unfortunately, CPR mustbe stopped for a reliable SAA analysis because chest compressions introduce artefacts in the ECG. Theseinterruptions in CPR have an adverse effect on OHCA survival. Since the early 1990s, many efforts havebeen made to reliably analyze the rhythm during CPR. Strategies have mainly focused on adaptive filtersto suppress the CPR artefact followed by SAAs of commercial defibrillators. However, these solutionsdid not meet the American Heart Association¿s (AHA) accuracy requirements for shock/no-shockdecisions. A recent approach, which replaces the commercial SAA by machine learning classifiers, hasdemonstrated that a reliable rhythm analysis during CPR is possible. However, defibrillation is not theonly treatment needed during OHCA, and depending on the clinical context a finer rhythm classificationis needed. Indeed, an optimal OHCA scenario would allow the classification of the five cardiac arrestrhythm types that may be present during resuscitation. Unfortunately, multiclass classifiers that allow areliable rhythm analysis during CPR have not yet been demonstrated. On all of these studies artefactsoriginate from manual compressions delivered by rescuers. Mechanical compression devices, such as theLUCAS or the AutoPulse, are increasingly used in resuscitation. Thus, a reliable rhythm analysis duringmechanical CPR is becoming critical. Unfortunately, no AHA compliant algorithms have yet beendemonstrated during mechanical CPR. The focus of this thesis work is to provide new or improvedsolutions for rhythm analysis during CPR, including shock/no-shock decision during manual andmechanical CPR and multiclass classification during manual CPR

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). Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest. Entropy. 20(8):1-25. https://doi.org/10.3390/e20080591S125208Gräsner, J.-T., Lefering, R., Koster, R. W., Masterson, S., Böttiger, B. W., Herlitz, J., … Maurer, H. (2016). EuReCa ONE⿿27 Nations, ONE Europe, ONE Registry. Resuscitation, 105, 188-195. doi:10.1016/j.resuscitation.2016.06.004Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., … Deo, R. (2018). Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation, 137(12). doi:10.1161/cir.0000000000000558Rubart, M. (2005). Mechanisms of sudden cardiac death. Journal of Clinical Investigation, 115(9), 2305-2315. doi:10.1172/jci26381Zoll, P. M. (1952). Resuscitation of the Heart in Ventricular Standstill by External Electric Stimulation. New England Journal of Medicine, 247(20), 768-771. doi:10.1056/nejm195211132472005Cobb, L. A. (1999). Influence of Cardiopulmonary Resuscitation Prior to Defibrillation in Patients With Out-of-Hospital Ventricular Fibrillation. JAMA, 281(13), 1182. doi:10.1001/jama.281.13.1182Wik, L., Hansen, T. B., Fylling, F., Steen, T., Vaagenes, P., Auestad, B. H., & Steen, P. A. (2003). Delaying Defibrillation to Give Basic Cardiopulmonary Resuscitation to Patients With Out-of-Hospital Ventricular Fibrillation. JAMA, 289(11), 1389. doi:10.1001/jama.289.11.1389Link, M. S., Atkins, D. L., Passman, R. S., Halperin, H. R., Samson, R. A., White, R. D., … Kerber, R. E. (2010). Part 6: Electrical Therapies: Automated External Defibrillators, Defibrillation, Cardioversion, and Pacing * 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation, 122(18_suppl_3), S706-S719. doi:10.1161/circulationaha.110.970954Takata, T. S., Page, R. L., & Joglar, J. A. (2001). Automated External Defibrillators: Technical Considerations and Clinical Promise. Annals of Internal Medicine, 135(11), 990. doi:10.7326/0003-4819-135-11-200112040-00011Figuera, C., Irusta, U., Morgado, E., Aramendi, E., Ayala, U., Wik, L., … Alonso-Atienza, F. (2016). Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators. PLOS ONE, 11(7), e0159654. doi:10.1371/journal.pone.0159654Telesz, B. J., Hess, E. P., Atkinson, E., & White, R. D. (2015). Recurrent ventricular fibrillation: Experience with first responders prior to advanced life support interventions. Resuscitation, 88, 138-142. doi:10.1016/j.resuscitation.2014.10.010Xie, J., Weil, M. H., Sun, S., Tang, W., Sato, Y., Jin, X., & Bisera, J. (1997). High-Energy Defibrillation Increases the Severity of Postresuscitation Myocardial Dysfunction. Circulation, 96(2), 683-688. doi:10.1161/01.cir.96.2.683Cheskes, S., Schmicker, R. H., Christenson, J., Salcido, D. D., Rea, T., Powell, J., … Morrison, L. (2011). Perishock Pause. Circulation, 124(1), 58-66. doi:10.1161/circulationaha.110.010736Reed, M. J., Clegg, G. R., & Robertson, C. E. (2003). Analysing the ventricular fibrillation waveform. Resuscitation, 57(1), 11-20. doi:10.1016/s0300-9572(02)00441-0Firoozabadi, R., Nakagawa, M., Helfenbein, E. D., & Babaeizadeh, S. (2013). Predicting defibrillation success in sudden cardiac arrest patients. Journal of Electrocardiology, 46(6), 473-479. doi:10.1016/j.jelectrocard.2013.06.007Ristagno, G., Li, Y., Fumagalli, F., Finzi, A., & Quan, W. (2013). Amplitude spectrum area to guide resuscitation—A retrospective analysis during out-of-hospital cardiopulmonary resuscitation in 609 patients with ventricular fibrillation cardiac arrest. Resuscitation, 84(12), 1697-1703. doi:10.1016/j.resuscitation.2013.08.017Callaway, C. W., & Menegazzi, J. J. (2005). Waveform analysis of ventricular fibrillation to predict defibrillation. Current Opinion in Critical Care, 11(3), 192-199. doi:10.1097/01.ccx.0000161725.71211.42He, M., Gong, Y., Li, Y., Mauri, T., Fumagalli, F., Bozzola, M., … Ristagno, G. (2015). Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests. Critical Care, 19(1). doi:10.1186/s13054-015-1142-zBrown, C. G., & Dzwonczyk, R. (1996). Signal Analysis of the Human Electrocardiogram During Ventricular Fibrillation: Frequency and Amplitude Parameters as Predictors of Successful Countershock. Annals of Emergency Medicine, 27(2), 184-188. doi:10.1016/s0196-0644(96)70346-3Sherman, L. D., Callaway, C. W., & Menegazzi, J. J. (2000). Ventricular fibrillation exhibits dynamical properties and self-similarity. Resuscitation, 47(2), 163-173. doi:10.1016/s0300-9572(00)00229-xWEAVER, W. D. (1985). Amplitude of Ventricular Fibrillation Waveform and Outcome After Cardiac Arrest. Annals of Internal Medicine, 102(1), 53. doi:10.7326/0003-4819-102-1-53Jekova, I., Mougeolle, F., & Valance, A. (2004). Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram. Physiological Measurement, 25(5), 1179-1188. doi:10.1088/0967-3334/25/5/008Wu, X., Bisera, J., & Tang, W. (2013). Signal integral for optimizing the timing of defibrillation. Resuscitation, 84(12), 1704-1707. doi:10.1016/j.resuscitation.2013.08.005Hamprecht, F. A., Jost, D., Rüttimann, M., Calamai, F., & Kowalski, J. J. (2001). Preliminary results on the prediction of countershock success with fibrillation power. Resuscitation, 50(3), 297-299. doi:10.1016/s0300-9572(01)00360-4Neurauter, A., Eftestøl, T., Kramer-Johansen, J., Abella, B. S., Sunde, K., Wenzel, V., … Strohmenger, H.-U. (2007). Prediction of countershock success using single features from multiple ventricular fibrillation frequency bands and feature combinations using neural networks. Resuscitation, 73(2), 253-263. doi:10.1016/j.resuscitation.2006.10.002Ristagno, G., Mauri, T., Cesana, G., Li, Y., Finzi, A., Fumagalli, F., … Pesenti, A. (2015). Amplitude Spectrum Area to Guide Defibrillation. Circulation, 131(5), 478-487. doi:10.1161/circulationaha.114.010989Eftestøl, T., Sunde, K., Ole Aase, S., Husøy, J. H., & Steen, P. A. (2000). Predicting Outcome of Defibrillation by Spectral Characterization and Nonparametric Classification of Ventricular Fibrillation in Patients With Out-of-Hospital Cardiac Arrest. Circulation, 102(13), 1523-1529. doi:10.1161/01.cir.102.13.1523Povoas, H. P., & Bisera, J. (2000). Electrocardiographic waveform analysis for predicting the success of defibrillation. Critical Care Medicine, 28(Supplement), N210-N211. doi:10.1097/00003246-200011001-00010Podbregar, M., Kovačič, M., Podbregar-Marš, A., & Brezocnik, M. (2003). Predicting defibrillation success by ‘genetic’ programming in patients with out-of-hospital cardiac arrest. Resuscitation, 57(2), 153-159. doi:10.1016/s0300-9572(03)00030-3Callaway, C. W., Sherman, L. D., Mosesso, V. N., Dietrich, T. J., Holt, E., & Clarkson, M. C. (2001). Scaling Exponent Predicts Defibrillation Success for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. Circulation, 103(12), 1656-1661. doi:10.1161/01.cir.103.12.1656Sherman, L. D., Rea, T. D., Waters, J. D., Menegazzi, J. J., & Callaway, C. W. (2008). Logarithm of the absolute correlations of the ECG waveform estimates duration of ventricular fibrillation and predicts successful defibrillation. Resuscitation, 78(3), 346-354. doi:10.1016/j.resuscitation.2008.04.009Lin, L.-Y., Lo, M.-T., Ko, P. C.-I., Lin, C., Chiang, W.-C., Liu, Y.-B., … Ma, M. H.-M. (2010). Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest. Resuscitation, 81(3), 297-301. doi:10.1016/j.resuscitation.2009.12.003Gong, Y., Lu, Y., Zhang, L., Zhang, H., & Li, Y. (2015). Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. BioMed Research International, 2015, 1-7. doi:10.1155/2015/493472Watson, J. N., Uchaipichat, N., Addison, P. S., Clegg, G. R., Robertson, C. E., Eftestol, T., & Steen, P. A. (2004). Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods. Resuscitation, 63(3), 269-275. doi:10.1016/j.resuscitation.2004.06.012Gundersen, K., Kvaløy, J. T., Kramer-Johansen, J., & Eftestøl, T. (2008). Identifying approaches to improve the accuracy of shock outcome prediction for out-of-hospital cardiac arrest. Resuscitation, 76(2), 279-284. doi:10.1016/j.resuscitation.2007.07.019Howe, A., Escalona, O. J., Di Maio, R., Massot, B., Cromie, N. A., Darragh, K. M., … McEneaney, D. J. (2014). A support vector machine for predicting defibrillation outcomes from waveform metrics. Resuscitation, 85(3), 343-349. doi:10.1016/j.resuscitation.2013.11.021Indik, J. H., Conover, Z., McGovern, M., Silver, A. E., Spaite, D. W., Bobrow, B. J., & Kern, K. B. (2014). Association of Amplitude Spectral Area of the Ventricular Fibrillation Waveform With Survival of Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. Journal of the American College of Cardiology, 64(13), 1362-1369. doi:10.1016/j.jacc.2014.06.1196Coult, J., Sherman, L., Kwok, H., Blackwood, J., Kudenchuk, P. J., & Rea, T. D. (2016). Short ECG segments predict defibrillation outcome using quantitative waveform measures. Resuscitation, 109, 16-20. doi:10.1016/j.resuscitation.2016.09.020Endoh, H., Hida, S., Oohashi, S., Hayashi, Y., Kinoshita, H., & Honda, T. (2010). Prompt prediction of successful defibrillation from 1-s ventricular fibrillation waveform in patients with out-of-hospital sudden cardiac arrest. Journal of Anesthesia, 25(1), 34-41. doi:10.1007/s00540-010-1043-xChicote, B., Irusta, U., Alcaraz, R., Rieta, J., Aramendi, E., Isasi, I., … Ibarguren, K. (2016). Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy, 18(9), 313. doi:10.3390/e18090313Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Weiting Chen, Zhizhong Wang, Hongbo Xie, & Wangxin Yu. (2007). Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266-272. doi:10.1109/tnsre.2007.897025Xiao-Feng, L., & Yue, W. (2009). Fine-grained permutation entropy as a measure of natural complexity for time series. Chinese Physics B, 18(7), 2690-2695. doi:10.1088/1674-1056/18/7/011Fadlallah, B., Chen, B., Keil, A., & Príncipe, J. (2013). Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information. Physical Review E, 87(2). doi:10.1103/physreve.87.022911Eftestøl, T., Sunde, K., & Steen, P. A. (2002). Effects of Interrupting Precordial Compressions on the Calculated Probability of Defibrillation Success During Out-of-Hospital Cardiac Arrest. Circulation, 105(19), 2270-2273. doi:10.1161/01.cir.0000016362.42586.feEdelson, D. P., Abella, B. S., Kramer-Johansen, J., Wik, L., Myklebust, H., Barry, A. M., … Becker, L. B. (2006). Effects of compression depth and pre-shock pauses predict defibrillation failure during cardiac arrest. Resuscitation, 71(2), 137-145. doi:10.1016/j.resuscitation.2006.04.008Ibarguren, K., Unanue, J. M., Alonso, D., Vaqueriza, I., Irusta, U., Aramendi, E., & Chicote, B. (2015). Difference in survival from pre-hospital cardiac arrest between cities and villages in the Basque Autonomous Community. Resuscitation, 96, 114. doi:10.1016/j.resuscitation.2015.09.269Jacobs, I., Nadkarni, V., Bahr, J., Berg, R. A., Billi, J. E., Bossaert, L., … Zideman, D. (2004). Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries. Resuscitation, 63(3), 233-249. doi:10.1016/j.resuscitation.2004.09.008Rittenberger, J. C., Raina, K., Holm, M. B., Kim, Y. J., & Callaway, C. W. (2011). Association between Cerebral Performance Category, Modified Rankin Scale, and discharge disposition after cardiac arrest. Resuscitation, 82(8), 1036-1040. doi:10.1016/j.resuscitation.2011.03.034Marn-Pernat, A., Weil, M. H., Tang, W., Pernat, A., & Bisera, J. (2001). Optimizing timing of ventricular defibrillation. Critical Care Medicine, 29(12), 2360-2365. doi:10.1097/00003246-200112000-00019Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297-2301. doi:10.1073/pnas.88.6.2297Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61-68. doi:10.1016/j.medengphy.2008.04.005Alcaraz, R., Abásolo, D., Hornero, R., & Rieta, J. J. (2010). Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Computer Methods and Programs in Biomedicine, 99(1), 124-132. doi:10.1016/j.cmpb.2010.02.009Zou, K. H., O’Malley, A. J., & Mauri, L. (2007). Receiver-Operating Characteristic Analysis for Evaluating Diagnostic Tests and Predictive Models. Circulation, 115(5), 654-657. doi:10.1161/circulationaha.105.594929Perkins, N. J., & Schisterman, E. F. (2006). The Inconsistency of «Optimal» Cutpoints Obtained using Two Criteria based on the Receiver Operating Characteristic Curve. American Journal of Epidemiology, 163(7), 670-675. doi:10.1093/aje/kwj063Monsieurs, K. G., Nolan, J. P., Bossaert, L. L., Greif, R., Maconochie, I. K., Nikolaou, N. I., … Wyllie, J. (2015). European Resuscitation Council Guidelines for Resuscitation 2015. Resuscitation, 95, 1-80. doi:10.1016/j.resuscitation.2015.07.038Ruiz, J., Ayala, U., de Gauna, S. R., Irusta, U., González-Otero, D., Alonso, E., … Eftestøl, T. (2013). Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation. Resuscitation, 84(9), 1223-1228. doi:10.1016/j.resuscitation.2013.01.034Ayala, U., Irusta, U., Ruiz, J., Ruiz de Gauna, S., González-Otero, D., Alonso, E., … Eftestøl, T. (2015). Fully automatic rhythm analysis during chest compression pauses. Resuscitation, 89, 25-30. doi:10.1016/j.resuscitation.2014.11.022Singh, A., Saini, B. S., & Singh, D. (2015). An alternative approach to approximate entropy threshold value (r) selection: application to heart rate variability and systolic blood pressure variability under postural challenge. Medical & Biological Engineering & Computing, 54(5), 723-732. doi:10.1007/s11517-015-1362-zNeurauter, A., Eftestøl, T., Kramer-Johansen, J., Abella, B. S., Wenzel, V., Lindner, K. H., … Strohmenger, H.-U. (2008). Improving countershock success prediction during cardiopulmonary resuscitation using ventricular fibrillation features from higher ECG frequency bands. Resuscitation, 79(3), 453-459. doi:10.1016/j.resuscitation.2008.07.024Rosso, O. A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schürmann, M., & Başar, E. (2001). Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods, 105(1), 65-75. doi:10.1016/s0165-0270(00)00356-3Weaver, B., & Wuensch, K. L. (2013). SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients. Behavior Research Methods, 45(3), 880-895. doi:10.3758/s13428-012-0289-7Sherman, L. D. (2006). The frequency ratio: An improved method to estimate ventricular fibrillation duration based on Fourier analysis of the waveform. Resuscitation, 69(3), 479-486. doi:10.1016/j.resuscitation.2005.09.024Weisfeldt, M. L., & Becker, L. B. (2002). Resuscitation After Cardiac Arrest. JAMA, 288(23), 3035. doi:10.1001/jama.288.23.3035Gazmuri, R. J., Berkowitz, M., & Cajigas, H. (1999). Myocardial effects of ventricular fibrillation in the isolated rat heart. Critical Care Medicine, 27(8), 1542-1550. doi:10.1097/00003246-199908000-00023JARDETZKY, O., GREENE, E. A., & LORBER, V. (1956). Oxygen Consumption of the Completely Isolated Dog Heart In Fibrillation. Circulation Research, 4(2), 144-147. doi:10.1161/01.res.4.2.144Hoogendijk, M. G., Schumacher, C. A., Belterman, C. N. W., Boukens, B. J., Berdowski, J., de Bakker, J. M. T., … Coronel, R. (2012). Ventricular fibrillation hampers the restoration of creatine-phosphate levels during simulated cardiopulmonary resuscitations. EP Europace, 14(10), 1518-1523. doi:10.1093/europace/eus078Neumar, R. W., Brown, C. G., Van Ligten, P., Hoekstra, J., Altschuld, R. A., & Baker, P. (1991). Estimation of myocardial ischemic injury during ventricular fibrillation with total circulatory arrest using high-energy phosphates and lactate as metabolic markers. Annals of Emergency Medicine, 20(3), 222-229. doi:10.1016/s0196-0644(05)80927-8Kern, K. B., Garewal, H. S., Sanders, A. B., Janas, W., Nelson, J., Sloan, D., … Ewy, G. A. (1990). Depletion of myocardial adenosine triphosphate during prolonged untreated ventricular fibrillation: effect on defibrillation success. Resuscitation, 20(3), 221-229. doi:10.1016/0300-9572(90)90005-yChoi, H. J., Nguyen, T., Park, K. S., Cha, K. C., Kim, H., Lee, K. H., & Hwang, S. O. (2013). Effect of cardiopulmonary resuscitation on restoration of myocardial ATP in prolonged ventricular fibrillation. Resuscitation, 84(1), 108-113. doi:10.1016/j.resuscitation.2012.06.006Salcido, D. D., Menegazzi, J. J., Suffoletto, B. P., Logue, E. S., & Sherman, L. D. (2009). Association of intramyocardial high energy phosphate concentrations with quantitative measures of the ventricular fibrillation electrocardiogram waveform. Resuscitation, 80(8), 946-950. doi:10.1016/j.resuscitation.2009.05.002Reynolds, J. C., Salcido, D. D., & Menegazzi, J. J. (2012). Correlation between coronary perfusion pressure and quantitative ECG waveform measures during resuscitation of prolonged ventricular fibrillation. Resuscitation, 83(12), 1497-1502. doi:10.1016/j.resuscitation.2012.04.013Didon, J.-P., Krasteva, V., Ménétré, S., Stoyanov, T., & Jekova, I. (2011). Shock advisory system with minimal delay triggering after end of chest compressions: Accuracy and gained hands-off time. Resuscitation, 82, S8-S15. doi:10.1016/s0300-9572(11)70145-9Ruiz de Gauna, S., Irusta, U., Ruiz, J., Ayala, U., Aramendi, E., & Eftestøl, T. (2014). Rhythm Analysis during Cardiopulmonary Resuscitation: Past, Present, and Future. BioMed Research International, 2014, 1-13. doi:10.1155/2014/386010Manis, G., Aktaruzzaman, M., & Sassi, R. (2018). Low Computational Cost for Sample Entropy. Entropy, 20(1), 61. doi:10.3390/e20010061Snyder, D., & Morgan, C. (2004). Wide variation in cardiopulmonary resuscitation interruption intervals among commercially available automated external defibrillators may affect survival despite high defibrillation efficacy. Critical Care Medicine, 32(Supplement), S421-S424. doi:10.1097/01.ccm.0000134265.35871.2bMenegazzi, J. J., Callaway, C. W., Sherman, L. D., Hostler, D. P., Wang, H. E., Fertig, K. C., & Logue, E. S. (2004). Ventricular Fibrillation Scaling Exponent Can Guide Timing of Defibrillation and Other Therapies. Circulation, 109(7), 926-931. doi:10.1161/01.cir.0000112606.41127.d2Lombardi, F. (2001). Sudden cardiac death: role of heart rate variability to identify patients at risk. Cardiovascular Research, 50(2), 210-217. doi:10.1016/s0008-6363(01)00221-8Moorman, J. R., Carlo, W. A., Kattwinkel, J., Schelonka, R. L., Porcelli, P. J., Navarrete, C. T., … Michael O’Shea, T. (2011). Mortality Reduction by Heart Rate Characteristic Monitoring in Very Low Birth Weight Neonates: A Randomized Trial. The Journal of Pediatrics, 159(6), 900-906.e1. doi:10.1016/j.jpeds.2011.06.044Sessa, F., Anna, V., Messina, G., Cibelli, G., Monda, V., Marsala, G., … Salerno, M. (2018). Heart rate variability as predictive factor for sudden cardiac death. Aging, 10(2), 166-1