Prediction of the Outcome in Cardiac Arrest Patients Undergoing Hypothermia Using EEG Wavelet Entropy

Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients’ outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64-100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (pvalue ≤ 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia

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

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.This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades through grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), and by the Basque Government through grants IT1229-19, PRE_2019_2_0100 and PRE_2019_1_0262. A.I. receives research grants from the US National Institutes of Health (NIH)

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary Artery Disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the Heart Rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD

Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night

Peer ReviewedPostprint (author's final draft

Tehohoitopotilaiden neuromonitorointi

In critical illness the risk of neurological insults is high, whether because of the illness itself, or as a treatment complication. As a result, the length of hospital stay and the risk of both further morbidity and mortality are all roughly doubled. One of the major challenges is the inability to monitor a sedated, mechanically ventilated patient’s neurological symptoms during intensive care treatment, due to a lack of reliable methods. The aims of this thesis research were to identify and test potential non-invasive methods, which would be predictive of neurological outcome, showing potential as neuromonitoring methods of critical care patients unable to self-report. As a guiding theme, all tested methods could be applied to actual critical care with relative ease. Patients were included from two groups with a notably high incidence of neurological complications, namely acute liver failure patients with hepatic encephalopathy (I), and aortic surgery patients operated during hypothermic circulatory arrest (II). The first group included 20 patients, and the latter 30 patients. Late mortality and quality of life was assessed for the aortic surgery patients (III), and the postoperative development of certain blood biomarkers (IV). The tested non-invasive neuromonitoring methods included electroencephalogram (EEG) variables from frontal or fronto-temporal abbreviated monitoring, frontal near-infrared spectroscopy, transcranial Doppler ultrasound measurements of the intracranial blood flow, and finally biomarkers. The last included established biomarkers with an association with neurological complications, namely neuron-specific enolase, and protein S100β, and several interesting biomarkers normally associated with tumours and pancreatitis. Of the tested methods, the frontal EEG variables showed greatest promise, but the addition of the temporal channels did not increase sensitivity. Spectral EEG variables were predictive of the stage of hepatic encephalopathy (I), while a novel EEG variable called wavelet subband entropy was predictive of neurological outcome (I). The hemispheric asymmetry of frontal EEG was reasonably predictive of neurological outcome after aortic surgery (II). None of the other tested methods were predictive of outcome (I, II, IV), except protein S100β, which was significantly higher in the poor outcome group 48 to 72 hours after hypothermic circulatory arrest (II). The quality of life of aortic surgery patients was good after 5 to 8 years, and comparable with the general population of chronically ill patients (III). The aim of this explorative research was to identify and test non-invasive neuromonitoring methods, suitable for use in critical care. Based on the results, frontal EEG variables are promising and predict the grade of hepatic encephalopathy and neurological outcome. The other tested methods were not predictive of neurological outcome. The long-term quality of life of aortic surgery patients is very good, despite the high risk for neurological complications.Kriittisissä sairauksissa neurologisen komplikaation riski on suuri, sekä itse kriittisen sairauden että varsinaisen hoidon seurauksena. Haittatapahtuman johdosta sairaalahoidon kesto sekä sairastuvuuden ja kuolleisuuden riskit kaksinkertaistuvat. Yksi suurimmista haasteista on luotettavien menetelmien puute, joilla voitaisiin arvioida mekaanisen hengitystuen varassa olevan ja rauhoittavia lääkkeitä saavan potilaan neurologisia oireita tehohoidon aikana. Tämän väitöskirjatyön tarkoituksena oli tunnistaa ja testata lupaavia ei-kajoavia menetelmiä, jotka ennustaisivat neurologista lopputulosta, ja jotka soveltuisivat kriittisesti sairaan tehohoitopotilaan neuromonitorointiin. Kantavana teemana kaikki testatut menetelmät voitaisiin soveltaa kliiniseen työhön suhteellisen helposti. Potilaita kerättiin kahteen ryhmään, joissa neurologisten komplikaatioiden esiintyvyys on huomattavan suuri. Ensimmäinen ryhmä käsitti akuuttia maksan vajaatoimintaa ja hepaattista enkefalopatiaa sairastavat potilaat (I), toinen hypotermisen verenkierron pysäytyksen aikana rinta-aortan leikkauksen läpikäyvät potilaat (II). Ensimmäiseen ryhmään kuului 20 potilasta, jälkimmäiseen 30 potilasta. Aorttaleikatuilta potilailta arvioitiin myös elämänlaatua sekä myöhäiskuolleisuutta (III), lisäksi tiettyjen biomerkkiaineiden aorttaleikkauksen jälkeistä kehitystä ja soveltuvuutta neuromonitorointiin arvioitiin yhdessä osatyössä (IV). Tutkimuksessa arvioituihin ei-kajoaviin neuromonitorointimenetelmiin lukeutuivat otsa- ja ohimolohkon elektroenkefalografia (EEG), lähi-infrapunaspektroskopia, transkraniaalinen Doppler-ultraäänimittaus sekä verestä mitattavat biomerkkiaineet. Biomerkkiaineet kattoivat sekä vakiintuneita aivovauriota heijastavia merkkiaineita (hermostoperäinen enolaasi, proteiini S100β) että useita mielenkiintoisia merkkiaineita, jotka liittyvät kasvaintauteihin ja haimatulehdukseen. Testatuista menetelmistä otsalohkon EEG muuttujat olivat lupaavia, mutta ohimolohkon EEG lisääminen ei parantanut menetelmien herkkyyttä. EEG spektrimuuttujat ennustivat hepaattisen enkefalopatian astetta (I) luotettavasti, kun taas kokeellinen EEG-muuttuja (aalloke-alitaajuuden entropia) ennusti luotettavasti neurologista lopputulosta akuutin maksan vajaatoimintaa sairastavilla potilailla (I). Otsalohkon aivopuoliskojen EEG-rekisteröinnin hetkellinen epäsymmetria ennusti kohtalaisella tarkkuudella neurologisten päätetapahtumien esiintymisen aorttaleikatuilla potilailla (II). Muut testatut menetelmät eivät ennustaneet neurologista lopputulemaa (I, II, IV), paitsi proteiini S100β, joka oli merkittävästi korkeampi 48–72 tuntia leikkauksen jälkeen niillä potilailla, joiden neurologinen toipuminen oli huono (IV). Aorttaleikattujen potilaiden elämänlaatu oli hyvä 5–8 vuotta leikkauksen jälkeen ja verrattavissa kroonisesti sairaan väestön elämänlaatuun (III). Tämän kartoittavan tutkimuksen tarkoituksena oli tunnistaa ja testata ei-kajoavia neuromonitorointimenetelmiä, jotka soveltuvat tehohoitoon. Tulosten perusteella otsalohkon EEG-muuttujat ennustavat hepaattisen enkefalopatian astetta sekä potilaan neurologista toipumista. Muut testatut menetelmät eivät ennustaneet neurologista toipumista luotettavasti. Aorttaleikattujen potilaiden pitkäaikainen (5–8 vuoden) terveyteen liittyvä elämänlaatu on erittäin hyvä, vaikka leikkaukseen liittyy korkea aivovaurion riski

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award DP2-OD006454)National Institutes of Health (U.S.) (Award K08-GM094394)Burroughs Wellcome Fund (Award 1010625

Characterization of the autonomic nervous system response under emotional stimuli through linear and non-linear analysis of physiological signals

En esta disertación se presentan metodologías lineales y no lineales aplicadas a señales fisiológicas, con el propósito de caracterizar la respuesta del sistema nervioso autónomo bajo estímulos emocionales. Este estudio está motivado por la necesidad de desarrollar una herramienta que identifique emociones en función de su efecto sobre la actividad cardíaca, ya que puede tener un impacto potencial en la práctica clínica para diagnosticar enfermedades psico-neuronales.Las hipótesis de esta tesis doctoral son que las emociones inducen cambios notables en el sistema nervioso autónomo y que estos cambios pueden capturarse a partir del análisis de señales fisiológicas, en particular, del análisis conjunto de la variabilidad del ritmo cardíaco (HRV) y la respiración.La base de datos analizada contiene el registro simultáneo del electrocardiograma y la respiración de 25 sujetos elicitados con emociones inducidas por vídeos, incluyendo las siguientes emociones: alegría, miedo, tristeza e ira.En esta disertación se describen dos estudios metodológicos.En el primer estudio se propone un método basado en el análisis lineal de la HRV guiado por la respiración. El método se basó en la redefinición de la banda de alta frecuencia (HF), no solo centrándose en la frecuencia respiratoria, sino también considerando un ancho de banda que dependiera del espectro respiratorio. Primero, el método se validó con señales de HRV simuladas, obteniéndose errores mínimos de estimación en comparación con la definición de la banda de HF clásica e incluso con la banda de HF centrada en la frecuencia respiratoria pero con un ancho de banda constante, independientemente de los valores del ratio simpático-vagal.Después, el método propuesto se aplicó en una base de datos de elicitación emocional inducida mediante vídeos para discriminar entre emociones. No solo la banda de HF redefinida propuesta superó a las otras definiciones de banda de HF en discriminación emocional, sino también la correlación máxima entre los espectros de la HRV y de la respiración discriminó alegría y relajación, alegría y cada emoción de valencia negativa y entre miedo y tristeza con un p-valor ≤ 0.05 y AUC ≥ 0.70.En el segundo estudio, técnicas no lineales como la Función de Auto Información Mutua y la Función de Información Mutua Cruzada, AMIF y CMIF respectivamente, son también propuestas en esta tesis doctoral para el reconocimiento de emociones humanas. La técnica AMIF se aplicó a las señales de HRV para estudiar interdependencias complejas, y se consideró la técnica CMIF para cuantificar el acoplamiento complejo entre las señales de HRV y de respiración. Ambos algoritmos se adaptaron a las series temporales RR de corta duración. Las series RR fueron filtradas en las bandas de baja y alta frecuencia, y también se investigaron las series RR filtradas en un ancho de banda basado en la respiración.Los resultados revelaron que la técnica AMIF aplicada a la serie temporal RR filtrada en la banda de HF redefinida fue capaz de discriminar entre: relajación y alegría y miedo, alegría y cada valencia negativa y finalmente miedo y tristeza e ira, todos con un nivel de significación estadística (p-valor ≤ 0.05, AUC ≥ 0.70). Además, los parámetros derivados de AMIF y CMIF permitieron caracterizar la baja complejidad que la señal presentaba durante el miedo frente a cualquier otro estado emocional estudiado.Finalmente se investiga, mediante un clasificador lineal, las características lineales y no lineales que discriminan entre pares de emociones y entre valencias emocionales para determinar qué parámetros permiten diferenciar los grupos y cuántos de éstos son necesarios para lograr la mejor clasificación posible. Los resultados extraídos de este capítulo sugieren que pueden ser clasificadas mediante el análisis de la HRV: relajación y alegría, la valencia positiva frente a todas las negativas, alegría y miedo, alegría y tristeza, alegría e ira, y miedo y tristeza.El análisis conjunto de la HRV y la respiración aumenta la capacidad discriminatoria de la HRV, siendo la máxima correlación entre los espectros de la HRV y la respiración uno de los mejores índices para la discriminación de emociones. El análisis de la información mutua, aun en señales de corta duración, añade información relevante a los índices lineales para la discriminación de emociones.<br /