Cuantificación de la recurrencia en el estudio de la variabilidad del ritmo cardiaco y la duración del ciclo respiratorio en pacientes en proceso de extubación

El sistema nervioso autónomo regula el comportamiento de los sistemas cardiaco y respiratorio. Su evaluación durante la retirada de la ventilación mecánica puede proporcionar información sobre el comportamiento cardiorespiratorio de los pacientes. Este trabajo propone el análisis de la variabilidad del ritmo cardiaco (HRV) y la duración del ciclo respiratorio (TTot) aplicando la técnica ‘Recurrence Plot (RP)’ y su interacción ‘Joint Recurrence Plot (JRP)’. Se han analizado 131 pacientes, asistidos mediante ventilación mecánica, en proceso de extubación: 92 pacientes con éxito en la extubación (grupo E) y 39 pacientes que no pudieron mantener la respiración espontánea y fracasaron en la extubación (grupo F). Obtenida la matriz de recurrencia para cada señal, se calcularon parámetros que permitían cuantificar la recurrencia de éstas. Los resultados muestran que parámetros como el determinismo (DET), la duración media de la línea diagonal (L), y la entropía (ENTR), presentaron diferencias estadísticamente significativas aplicando RP en las series TTot, pero no en HRV. Al comparar la interacción entre los grupos con JRP, todos los parámetros han sido relevantes. En todos los casos, valores medios del análisis de la cuantificación de recurrencia es mayor en el grupo E que en el grupo F. Las principales diferencias entre los grupos se encuentran en las estructuras diagonales y verticales de la recurrencia conjunta.Postprint (published version

Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation

In this work we characterized the non-linear complexity of Heart Rate Variability (HRV) in short time series. The complexity of HRV signal was evaluated during emotional visual elicitation by using Dominant Lyapunov Exponents (DLEs) and Approximate Entropy (ApEn). We adopted a simplified model of emotion derived from the Circumplex Model of Affects (CMAs), in which emotional mechanisms are conceptualized in two dimensions by the terms of valence and arousal. Following CMA model, a set of standardized visual stimuli in terms of arousal and valence gathered from the International Affective Picture System (IAPS) was administered to a group of 35 healthy volunteers. Experimental session consisted of eight sessions alternating neutral images with high arousal content images. Several works can be found in the literature showing a chaotic dynamics of HRV during rest or relax conditions. The outcomes of this work showed a clear switching mechanism between regular and chaotic dynamics when switching from neutral to arousal elicitation. Accordingly, the mean ApEn decreased with statistical significance during arousal elicitation and the DLE became negative. Results showed a clear distinction between the neutral and the arousal elicitation and could be profitably exploited to improve the accuracy of emotion recognition systems based on HRV time series analysis

A methodology to quantify the differences between alternative methods of heart rate variability measurement

This work proposes a systematic procedure to report the differences between heart rate variability time series obtained from alternative measurements reporting the spread and mean of the differences as well as the agreement between measuring procedures and quantifying how stationary, random and normal the differences between alternative measurements are. A description of the complete automatic procedure to obtain a differences time series (DTS) from two alternative methods, a proposal of a battery of statistical tests, and a set of statistical indicators to better describe the differences in RR interval estimation are also provided. Results show that the spread and agreement depend on the choice of alternative measurements and that the DTS cannot be considered generally as a white or as a normally distributed process. Nevertheless, in controlled measurements the DTS can be considered as a stationary process.Peer ReviewedPostprint (published version

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

Background: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded at https://bitbucket.org/deepak_panday/ceps/src/pipeline_v2/. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing

Characterization of complex fractionated atrial electrograms by Sample Entropy: An international multi-center study

Atrial fibrillation (AF) is the most commonly clinically-encountered arrhythmia. Catheter ablation of AF is mainly based on trigger elimination and modification of the AF substrate. Substrate mapping ablation of complex fractionated atrial electrograms (CFAEs) has emerged to be a promising technique. To improve substrate mapping based on CFAE analysis, automatic detection algorithms need to be developed in order to simplify and accelerate the ablation procedures. According to the latest studies, the level of fractionation has been shown to be promisingly well estimated from CFAE measured during radio frequency (RF) ablation of AF. The nature of CFAE is generally nonlinear and nonstationary, so the use of complexity measures is considered to be the appropriate technique for the analysis of AF records. This work proposes the use of sample entropy (SampEn), not only as a way to discern between non-fractionated and fractionated atrial electrograms (A-EGM), but also as a tool for characterizing the degree of A-EGM regularity, which is linked to changes in the AF substrate and to heart tissue damage. The use of SampEn combined with a blind parameter estimation optimization process enables the classification between CFAE and non-CFAE with statistical significance (p < 0:001), 0.89 area under the ROC, 86% specificity and 77% sensitivity over a mixed database of A-EGM combined from two independent CFAE signal databases, recorded during RF ablation of AF in two EU countries (542 signals in total). On the basis of the results obtained in this study, it can be suggested that the use of SampEn is suitable for real-time support during navigation of RF ablation of AF, as only 1.5 seconds of signal segments need to be analyzed.This work has been supported by the Spanish Ministry of Science and Innovation, Research Project TEC 2009-14222, by the Ministry of Education Youth and Sports of the Czech Republic, the Grant Agency of the Czech Technical University in Prague No. SGS13/203/OHK3/3T/13 and by the Czech Science 300 Foundation post-doctoral GACR research project GACR #P103/11/P106.Cirugeda Roldán, EM.; Novak, D.; Kremen, V.; Cuesta Frau, D.; Keller, M.; Luik, A.; Srutova, M. (2015). Characterization of complex fractionated atrial electrograms by Sample Entropy: An international multi-center study. Entropy. 17(11):7493-7509. https://doi.org/10.3390/e17117493S749375091711Haïssaguerre, M., Jaïs, P., Shah, D. C., Takahashi, A., Hocini, M., Quiniou, G., … Clémenty, J. (1998). Spontaneous Initiation of Atrial Fibrillation by Ectopic Beats Originating in the Pulmonary Veins. New England Journal of Medicine, 339(10), 659-666. doi:10.1056/nejm199809033391003Nademanee, K., Schwab, M., Porath, J., & Abbo, A. (2006). How to perform electrogram-guided atrial fibrillation ablation. Heart Rhythm, 3(8), 981-984. doi:10.1016/j.hrthm.2006.03.018PORTER, M., SPEAR, W., AKAR, J. G., HELMS, R., BRYSIEWICZ, N., SANTUCCI, P., & WILBER, D. J. (2008). Prospective Study of Atrial Fibrillation Termination During Ablation Guided by Automated Detection of Fractionated Electrograms. Journal of Cardiovascular Electrophysiology, 19(6), 613-620. doi:10.1111/j.1540-8167.2008.01189.xHaïssaguerre, M., Hocini, M., Sanders, P., Takahashi, Y., Rotter, M., Sacher, F., … Jaïs, P. (2006). Localized Sources Maintaining Atrial Fibrillation Organized by Prior Ablation. Circulation, 113(5), 616-625. doi:10.1161/circulationaha.105.546648Schmitt, C., Ndrepepa, G., Weber, S., Schmieder, S., Weyerbrock, S., Schneider, M., … Schömig, A. (2002). Biatrial multisite mapping of atrial premature complexes triggering onset of atrial fibrillation. The American Journal of Cardiology, 89(12), 1381-1387. doi:10.1016/s0002-9149(02)02350-0NDREPEPA, G., KARCH, M. R., SCHNEIDER, M. A. E., WEYERBROCK, S., SCHREIECK, J., DEISENHOFER, I., … SCHMITT, C. (2002). Characterization of Paroxysmal and Persistent Atrial Fibrillation in the Human Left Atrium During Initiation and Sustained Episodes. Journal of Cardiovascular Electrophysiology, 13(6), 525-532. doi:10.1046/j.1540-8167.2002.00525.xNademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., … Ngarmukos, T. (2004). A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate. Journal of the American College of Cardiology, 43(11), 2044-2053. doi:10.1016/j.jacc.2003.12.054Oral, H., Chugh, A., Good, E., Wimmer, A., Dey, S., Gadeela, N., … Morady, F. (2007). Radiofrequency Catheter Ablation of Chronic Atrial Fibrillation Guided by Complex Electrograms. Circulation, 115(20), 2606-2612. doi:10.1161/circulationaha.107.691386Kumagai, K. (2007). Patterns of activation in human atrial fibrillation. Heart Rhythm, 4(3), S7-S12. doi:10.1016/j.hrthm.2006.12.013Mainardi, L. T., Corino, V. D., Lombardi, L., Tondo, C., Mantica, M., Lombardi, F., & Cerutti, S. (2004). BioMedical Engineering OnLine, 3(1), 37. doi:10.1186/1475-925x-3-37RAVELLI, F., FAES, L., SANDRINI, L., GAITA, F., ANTOLINI, R., SCAGLIONE, M., & NOLLO, G. (2005). Wave Similarity Mapping Shows the Spatiotemporal Distribution of Fibrillatory Wave Complexity in the Human Right Atrium During Paroxysmal and Chronic Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 16(10), 1071-1076. doi:10.1111/j.1540-8167.2005.50008.xNG, J., & GOLDBERGER, J. J. (2007). Understanding and Interpreting Dominant Frequency Analysis of AF Electrograms. Journal of Cardiovascular Electrophysiology, 18(6), 680-685. doi:10.1111/j.1540-8167.2007.00832.xTakahashi, Y., O’Neill, M. D., Hocini, M., Dubois, R., Matsuo, S., Knecht, S., … Haïssaguerre, M. (2008). Characterization of Electrograms Associated With Termination of Chronic Atrial Fibrillation by Catheter Ablation. Journal of the American College of Cardiology, 51(10), 1003-1010. doi:10.1016/j.jacc.2007.10.056Křemen, V., Lhotská, L., Macaš, M., Čihák, R., Vančura, V., Kautzner, J., & Wichterle, D. (2008). A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation. Physiological Measurement, 29(12), 1371-1381. doi:10.1088/0967-3334/29/12/002Ciaccio, E. J., Biviano, A. B., Whang, W., Gambhir, A., & Garan, H. (2010). Different characteristics of complex fractionated atrial electrograms in acute paroxysmal versus long-standing persistent atrial fibrillation. Heart Rhythm, 7(9), 1207-1215. doi:10.1016/j.hrthm.2010.06.018LIN, Y.-J., LO, M.-T., LIN, C., CHANG, S.-L., LO, L.-W., HU, Y.-F., … CHEN, S.-A. (2012). Nonlinear Analysis of Fibrillatory Electrogram Similarity to Optimize the Detection of Complex Fractionated Electrograms During Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 24(3), 280-289. doi:10.1111/jce.12019NG, J., BORODYANSKIY, A. I., CHANG, E. T., VILLUENDAS, R., DIBS, S., KADISH, A. H., & GOLDBERGER, J. J. (2010). Measuring the Complexity of Atrial Fibrillation Electrograms. Journal of Cardiovascular Electrophysiology, 21(6), 649-655. doi:10.1111/j.1540-8167.2009.01695.xGanesan, A. N., Kuklik, P., Lau, D. H., Brooks, A. G., Baumert, M., Lim, W. W., … Sanders, P. (2013). Bipolar Electrogram Shannon Entropy at Sites of Rotational Activation. Circulation: Arrhythmia and Electrophysiology, 6(1), 48-57. doi:10.1161/circep.112.976654Jacquemet, V., & Henriquez, C. S. (2009). Genesis of complex fractionated atrial electrograms in zones of slow conduction: A computer model of microfibrosis. Heart Rhythm, 6(6), 803-810. doi:10.1016/j.hrthm.2009.02.026Jadidi, A. S., Duncan, E., Miyazaki, S., Lellouche, N., Shah, A. J., Forclaz, A., … Jaïs, P. (2012). Functional Nature of Electrogram Fractionation Demonstrated by Left Atrial High-Density Mapping. Circulation: Arrhythmia and Electrophysiology, 5(1), 32-42. doi:10.1161/circep.111.964197Ferrario, M., Signorini, M. G., Magenes, G., & Cerutti, S. (2006). Comparison of Entropy-Based Regularity Estimators: Application to the Fetal Heart Rate Signal for the Identification of Fetal Distress. IEEE Transactions on Biomedical Engineering, 53(1), 119-125. doi:10.1109/tbme.2005.859809Lewis, M. J., & Short, A. L. (2007). Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise. Physiological Measurement, 28(6), 731-744. doi:10.1088/0967-3334/28/6/011Al-Angari, H. M., & Sahakian, A. V. (2007). Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome. IEEE Transactions on Biomedical Engineering, 54(10), 1900-1904. doi:10.1109/tbme.2006.889772Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 283(3), R789-R797. doi:10.1152/ajpregu.00069.2002Cervigón, R., Moreno, J., Reilly, R. B., Millet, J., Pérez-Villacastín, J., & Castells, F. (2010). Entropy measurements in paroxysmal and persistent atrial fibrillation. Physiological Measurement, 31(7), 1011-1020. doi:10.1088/0967-3334/31/7/010Alcaraz, R., & Rieta, J. J. (2009). The application of nonlinear metrics to assess organization differences in short recordings of paroxysmal and persistent atrial fibrillation. Physiological Measurement, 31(1), 115-130. doi:10.1088/0967-3334/31/1/008Orozco-Duque, A., Novak, D., Kremen, V., & Bustamante, J. (2015). Multifractal analysis for grading complex fractionated electrograms in atrial fibrillation. Physiological Measurement, 36(11), 2269-2284. doi:10.1088/0967-3334/36/11/2269Ugarte, J. P., Orozco-Duque, A., Tobón, C., Kremen, V., Novak, D., Saiz, J., … Bustamante, J. (2014). Dynamic Approximate Entropy Electroanatomic Maps Detect Rotors in a Simulated Atrial Fibrillation Model. PLoS ONE, 9(12), e114577. doi:10.1371/journal.pone.0114577Richman, 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.h2039STILES, M. K., BROOKS, A. G., JOHN, B., WILSON, L., KUKLIK, P., DIMITRI, H., … SANDERS, P. (2008). The Effect of Electrogram Duration on Quantification of Complex Fractionated Atrial Electrograms and Dominant Frequency. Journal of Cardiovascular Electrophysiology, 19(3), 252-258. doi:10.1111/j.1540-8167.2007.01034.xVerma, A., Novak, P., Macle, L., Whaley, B., Beardsall, M., Wulffhart, Z., & Khaykin, Y. (2008). A prospective, multicenter evaluation of ablating complex fractionated electrograms (CFEs) during atrial fibrillation (AF) identified by an automated mapping algorithm: Acute effects on AF and efficacy as an adjuvant strategy. Heart Rhythm, 5(2), 198-205. doi:10.1016/j.hrthm.2007.09.027Schilling, C., Keller, M., Scherr, D., Oesterlein, T., Haïssaguerre, M., Schmitt, C., … Luik, A. (2015). Fuzzy decision tree to classify complex fractionated atrial electrograms. Biomedical Engineering / Biomedizinische Technik, 60(3). doi:10.1515/bmt-2014-0110Garcia-Gonzalez, M. A., Fernandez-Chimeno, M., & Ramos-Castro, J. (2009). Errors in the Estimation of Approximate Entropy and Other Recurrence-Plot-Derived Indices Due to the Finite Resolution of RR Time Series. IEEE Transactions on Biomedical Engineering, 56(2), 345-351. doi:10.1109/tbme.2008.2005951Konings, K. T., Kirchhof, C. J., Smeets, J. R., Wellens, H. J., Penn, O. C., & Allessie, M. A. (1994). High-density mapping of electrically induced atrial fibrillation in humans. Circulation, 89(4), 1665-1680. doi:10.1161/01.cir.89.4.1665Lake, D. E., & Moorman, J. R. (2011). Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. American Journal of Physiology-Heart and Circulatory Physiology, 300(1), H319-H325. doi:10.1152/ajpheart.00561.2010HOEKSTRA, B. P. T., DIKS, C. G. H., ALLESSIE, M. A., & GOEDB, J. (1995). 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Comparative study of entropy sensitivity to missing biosignal data

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.This work has been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222.Cirugeda Roldan, EM.; Cuesta Frau, D.; Miró Martínez, P.; Oltra Crespo, S. (2014). Comparative study of entropy sensitivity to missing biosignal data. Entropy. 16(11):5901-5918. doi:10.3390/e16115901S590159181611Garrett, D., Peterson, D. A., Anderson, C. W., & Thaut, M. H. (2003). Comparison of linear, nonlinear, and feature selection methods for eeg signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 141-144. doi:10.1109/tnsre.2003.814441Alcaraz, R. (2013). Journal of Medical and Biological Engineering, 33(3), 239. doi:10.5405/jmbe.1401Muller, K., Anderson, C. W., & Birch, G. E. (2003). Linear and nonlinear methods for brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 165-169. doi:10.1109/tnsre.2003.814484Gao, J., Hu, J., & Tung, W. (2011). Entropy measures for biological signal analyses. Nonlinear Dynamics, 68(3), 431-444. doi:10.1007/s11071-011-0281-2Peng, C. ‐K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 82-87. doi:10.1063/1.166141Alcaraz, R., & Rieta, J. J. (2010). A novel application of sample entropy to the electrocardiogram of atrial fibrillation. Nonlinear Analysis: Real World Applications, 11(2), 1026-1035. doi:10.1016/j.nonrwa.2009.01.047Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 283(3), R789-R797. doi:10.1152/ajpregu.00069.2002Richman, 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.h2039Pincus, S. M., Gladstone, I. M., & Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4), 335-345. doi:10.1007/bf01619355Abasolo, D., Hornero, R., Escudero, J., & Espino, P. (2008). A Study on the Possible Usefulness of Detrended Fluctuation Analysis of the Electroencephalogram Background Activity in Alzheimer’s Disease. IEEE Transactions on Biomedical Engineering, 55(9), 2171-2179. doi:10.1109/tbme.2008.923145Hwa, R. C., & Ferree, T. C. (2002). Scaling properties of fluctuations in the human electroencephalogram. Physical Review E, 66(2). doi:10.1103/physreve.66.021901Lee, J.-M., Kim, D.-J., Kim, I.-Y., Park, K.-S., & Kim, S. I. (2002). Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data. Computers in Biology and Medicine, 32(1), 37-47. doi:10.1016/s0010-4825(01)00031-2Jospin, M., Caminal, P., Jensen, E. W., Litvan, H., Vallverdu, M., Struys, M. M. R. F., … Kaplan, D. T. (2007). Detrended Fluctuation Analysis of EEG as a Measure of Depth of Anesthesia. IEEE Transactions on Biomedical Engineering, 54(5), 840-846. doi:10.1109/tbme.2007.893453Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6). doi:10.1103/physreve.64.061907Radhakrishnan, N., & Gangadhar, B. N. (1998). Estimating regularity in epileptic seizure time-series data. IEEE Engineering in Medicine and Biology Magazine, 17(3), 89-94. doi:10.1109/51.677174Burnsed, J., Quigg, M., Zanelli, S., & Goodkin, H. P. (2011). Clinical Severity, Rather Than Body Temperature, During the Rewarming Phase of Therapeutic Hypothermia Affect Quantitative EEG in Neonates With Hypoxic Ischemic Encephalopathy. Journal of Clinical Neurophysiology, 28(1), 10-14. doi:10.1097/wnp.0b013e318205134bDeffeyes, J. E., Harbourne, R. T., DeJong, S. L., Kyvelidou, A., Stuberg, W. A., & Stergiou, N. (2009). Use of information entropy measures of sitting postural sway to quantify developmental delay in infants. Journal of NeuroEngineering and Rehabilitation, 6(1), 34. doi:10.1186/1743-0003-6-34Moorman, J. R., Delos, J. B., Flower, A. A., Cao, H., Kovatchev, B. P., Richman, J. S., & Lake, D. E. (2011). Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring. Physiological Measurement, 32(11), 1821-1832. doi:10.1088/0967-3334/32/11/s08Zhang, D., Ding, H., Liu, Y., Zhou, C., Ding, H., & Ye, D. (2009). Neurodevelopment in newborns: a sample entropy analysis of electroencephalogram. Physiological Measurement, 30(5), 491-504. doi:10.1088/0967-3334/30/5/006Veiga, J., Lopes, A. J., Jansen, J. M., & Melo, P. L. (2011). Airflow pattern complexity and airway obstruction in asthma. Journal of Applied Physiology, 111(2), 412-419. doi:10.1152/japplphysiol.00267.2011Albuerne-Sanchez, L., Gonzalez-Camarena, R., Mejia-Avila, M., Carrillo-Rodriguez, G., Aljama-Corrales, T., & Charleston-Villalobos, S. (2013). Linear and Nonlinear Analysis of Base Lung Sound in Extrinsic Allergic Alveolitis Patients in Comparison to Healthy Subjects. Methods of Information in Medicine, 52(03), 266-276. doi:10.3414/me12-01-0037Jing Hu, Jianbo Gao, & Principe, J. C. (2006). Analysis of Biomedical Signals by the Lempel-Ziv Complexity: the Effect of Finite Data Size. IEEE Transactions on Biomedical Engineering, 53(12), 2606-2609. doi:10.1109/tbme.2006.883825Maestri, R., Pinna, G. D., Porta, A., Balocchi, R., Sassi, R., Signorini, M. G., … Raczak, G. (2007). Assessing nonlinear properties of heart rate variability from short-term recordings: are these measurements reliable? Physiological Measurement, 28(9), 1067-1077. doi:10.1088/0967-3334/28/9/008Hornero, R., Aboy, M., Abasolo, D., McNames, J., & Goldstein, B. (2005). Interpretation of Approximate Entropy: Analysis of Intracranial Pressure Approximate Entropy During Acute Intracranial Hypertension. 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