Estimación de la frecuencia respiratoria mediante análisis tiempo-frecuencia de la señal de variabilidad del ritmo cardiaco en condiciones no estacionarias

La influencia de la respiración sobre la señal electrocardiográfica (ECG) se manifiesta tanto en variaciones morfológicas de la misma como en una modulación del ritmo cardíaco, conocida como arritmia sinusal respiratoria (RSA), por lo que medidas basadas en el ECG pueden, de forma indirecta, proveer información de la respiración, que resulta de especial interés cuando el registro de la señal respiratoria es inviable o incómodo para el paciente. El objetivo de este trabajo fin de máster (TFM) es estimar la frecuencia respiratoria a partir del estudio tiempo-frecuencia (TF) de la señal de variabilidad del ritmo cardíaco (HRV) en condiciones no estacionarias. La recuencia respiratoria se estima como la componente de alta frecuencia (HF) de la HRV, que, a su vez es estimada mediante la localización para cada instante de tiempo del pico máximo de la distribución pseudo Wigner-Ville suavizada (SPWVD) de la HRV en la banda de HF. El método desarrollado en éste TFM utilizada para el cálculo de la SPWVD ventanas de filtrado frecuencial de longitud variable con el fin de minimizar el error cuadrático medio (MSE) de estimación de la frecuencia, en especial cuando las variaciones de ésta son no lineales. La longitud óptima de la ventana de filtrado frecuencial para cada instante de tiempo depende tanto de las variaciones de la frecuencia a estimar, como de la amplitud la componente de HF y del ruido presente en la señal, que es necesario estimar. En condiciones no estacionarias, no solo la frecuencia sino también la amplitud de la componente HF y el ruido pueden variar, por lo que se ha desarrollado un estimador de la amplitud instantánea de la componente HF a partir de la SPWVD con eliminación de la influencia de los filtrados temporal y frecuencial. También se ha desarrollado un estimador de la potencia instantánea del ruido presente en la señal que incluye los errores de estimación de la amplitud instantánea. Para el cálculo de la SPWVD se han utilizado diferentes kernels de filtrado tiempo-frecuencia formados por tres tipos de ventanas, rectangular, Hamming y exponencial, tanto en tiempo como en frecuencia. La evaluación del método se ha realizado tanto a través de un estudio de simulación, en el que se han generado señales con características tiempo-frecuencia similares a las de la HRV, variaciones no lineales de frecuencia y amplitudes variantes en el tiempo, como a través del análisis de una base de datos, que consta del registro simultáneo de la señales ECG y respiratorias de 58 sujetos sometidos a la escucha de diferentes estímulos musicales. El método propuesto en este TFM estima la amplitud instantánea de la componente de HF de la HRV sobre la señales simuladas con un error medio de 0.324±2.294% y su frecuencia con un error medio de -0.239±2.041% (-0.008±6.026 mHz). La estimación de la frecuencia respiratoria en señales reales presenta un error mediano de -1.525±4.557% (1.953±4.883 mHz) en los segmentos musicales y de -0.919±6.542% (11.465±43.477 mHz) en las transiciones entre segmentos musicales. Finalmente el método desarrollado en este TFM ha sido comparado con otros existentes en la literatura, basados en ventanas de filtrado frecuencial tanto de longitud fija como variable para amplitudes constantes

Evaluation of the different numerical formats for HIL models of power converters after the adoption of VHDL-2008 by xilinx

Hardware in the loop is a widely used technique in power electronics, allowing to test and debug in real time (RT) at a low cost. In this context, field-programmable gate arrays (FPGAs) play an important role due to the high-speed requirements of RT simulations, in which area optimization is also crucial. Both characteristics, area and speed, are affected by the numerical formats (NFs) and their rounding modes. Regarding FPGAs, Xilinx is one of the largest manufacturers in the world, offering Vivado as its main design suite, but it was not until the release of Vivado 2020.2 that support for the IEEE NF libraries of VHDL-2008 was included. This work presents an exhaustive evaluation of the performance of Vivado 2020.2 in terms of area and speed using the native IEEE libraries of VHDL-2008 regarding NF. Results show that even though fixed-point NFs optimize area and speed, if a user prefers the use of floating-point NFs, with this new release, it can be synthesized—which could not be done in previous versions of Vivado. Although support for the native IEEE libraries of VHDL-2008 was included in Vivado 2020.2, it still lacks some issues regarding NF conversion during synthesis while support for simulation is not yet includedThis research received no external fundin

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). Nonlinear Analysis of Epicardial Atrial Electrograms of Electrically Induced Atrial Fibrillation in Man. Journal of Cardiovascular Electrophysiology, 6(6), 419-440. doi:10.1111/j.1540-8167.1995.tb00416.

Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-&#945; algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.The authors would like to thank Universidad Autonoma de Manizales for financial support in the present work (Research project 328-038). This work has also been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222.Rodríguez-Sotelo, JL.; Osorio-Forero, A.; Jiménez-Rodríguez, A.; Cuesta Frau, D.; Cirugeda Roldán, EM.; Peluffo, D. (2014). Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques. Entropy. 16(12):6573-6589. https://doi.org/10.3390/e16126573S657365891612Saper, C. B., Fuller, P. M., Pedersen, N. P., Lu, J., & Scammell, T. E. (2010). Sleep State Switching. Neuron, 68(6), 1023-1042. doi:10.1016/j.neuron.2010.11.032RAUCHS, G., DESGRANGES, B., FORET, J., & EUSTACHE, F. (2005). The relationships between memory systems and sleep stages. Journal of Sleep Research, 14(2), 123-140. doi:10.1111/j.1365-2869.2005.00450.xLandmann, N., Kuhn, M., Piosczyk, H., Feige, B., Baglioni, C., Spiegelhalder, K., … Nissen, C. (2014). The reorganisation of memory during sleep. Sleep Medicine Reviews, 18(6), 531-541. doi:10.1016/j.smrv.2014.03.005Hublin, C., Partinen, M., Koskenvuo, M., & Kaprio, J. (2007). Sleep and Mortality: A Population-Based 22-Year Follow-Up Study. Sleep, 30(10), 1245-1253. doi:10.1093/sleep/30.10.1245The role of polysomnography in the differential diagnosis of chronic insomnia. (1988). American Journal of Psychiatry, 145(3), 346-349. doi:10.1176/ajp.145.3.346Steriade, M., McCormick, D., & Sejnowski, T. (1993). Thalamocortical oscillations in the sleeping and aroused brain. Science, 262(5134), 679-685. doi:10.1126/science.8235588DANKER-HOPFE, H., ANDERER, P., ZEITLHOFER, J., BOECK, M., DORN, H., GRUBER, G., … DORFFNER, G. (2009). Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. Journal of Sleep Research, 18(1), 74-84. doi:10.1111/j.1365-2869.2008.00700.xDanker-Hopfe, H., Kunz, D., Gruber, G., Klösch, G., Lorenzo, J. L., Himanen, S. L., … Dorffner, G. (2004). Interrater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders. Journal of Sleep Research, 13(1), 63-69. doi:10.1046/j.1365-2869.2003.00375.xVuckovic, A., Radivojevic, V., Chen, A. C. N., & Popovic, D. (2002). Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Medical Engineering & Physics, 24(5), 349-360. doi:10.1016/s1350-4533(02)00030-9Robert, C., Guilpin, C., & Limoge, A. (1998). Review of neural network applications in sleep research. Journal of Neuroscience Methods, 79(2), 187-193. doi:10.1016/s0165-0270(97)00178-7Ronzhina, M., Janoušek, O., Kolářová, J., Nováková, M., Honzík, P., & Provazník, I. (2012). Sleep scoring using artificial neural networks. Sleep Medicine Reviews, 16(3), 251-263. doi:10.1016/j.smrv.2011.06.003Subasi, A., & Erçelebi, E. (2005). Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78(2), 87-99. doi:10.1016/j.cmpb.2004.10.009Rodríguez-Sotelo, J. L., Peluffo-Ordoñez, D., Cuesta-Frau, D., & Castellanos-Domínguez, G. (2012). Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering. Computer Methods and Programs in Biomedicine, 108(1), 250-261. doi:10.1016/j.cmpb.2012.04.007Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C., & Oberye, J. J. L. (2000). Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering, 47(9), 1185-1194. doi:10.1109/10.867928Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Van Sweden, B., Kemp, B., Kamphuisen, H. A. C., & Van der Velde, E. A. (1990). Alternative Electrode Placement in (Automatic) Sleep Scoring (F pz-Cz/P z-Oz versus C4-At ). Sleep, 13(3), 279-283. doi:10.1093/sleep/13.3.279Mourtazaev, M. S., Kemp, B., Zwinderman, A. H., & Kamphuisen, H. A. C. (1995). Age and Gender Affect Different Characteristics of Slow Waves in the Sleep EEG. Sleep, 18(7), 557-564. doi:10.1093/sleep/18.7.557Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., & Dickhaus, H. (2012). Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier. Computer Methods and Programs in Biomedicine, 108(1), 10-19. doi:10.1016/j.cmpb.2011.11.005Shoupeng, S., & Peiwen, Q. (2007). A fractal-dimension-based signal-processing technique and its use for nondestructive testing. Russian Journal of Nondestructive Testing, 43(4), 270-280. doi:10.1134/s1061830907040080Peng, 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:10.1063/1.166141Fell, J., Röschke, J., Mann, K., & Schäffner, C. (1996). Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalography and Clinical Neurophysiology, 98(5), 401-410. doi:10.1016/0013-4694(96)95636-9Pincus, S. (1995). Approximate entropy (ApEn) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 110-117. doi:10.1063/1.166092Richman, 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.h2039Costa, M., Goldberger, A. L., & Peng, C.-K. (2005). Multiscale entropy analysis of biological signals. Physical Review E, 71(2). doi:10.1103/physreve.71.021906Hansen, P., & Mladenović, N. (2001). J-Means: a new local search heuristic for minimum sum of squares clustering. Pattern Recognition, 34(2), 405-413. doi:10.1016/s0031-3203(99)00216-2Güneş, S., Polat, K., & Yosunkaya, Ş. (2010). Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Systems with Applications, 37(12), 7922-7928. doi:10.1016/j.eswa.2010.04.043Koley, B., & Dey, D. (2012). An ensemble system for automatic sleep stage classification using single channel EEG signal. Computers in Biology and Medicine, 42(12), 1186-1195. doi:10.1016/j.compbiomed.2012.09.012Sleep stage classification using Wavelet Transform and neural networkhttp://www.researchgate.net/publication/216570220_Sleep_Stage_Classification_Using_Wavelet_Transform__Neural_NetworkKrakovská, A., & Mezeiová, K. (2011). Automatic sleep scoring: A search for an optimal combination of measures. Artificial Intelligence in Medicine, 53(1), 25-33. doi:10.1016/j.artmed.2011.06.004SHAMBROOM, J. R., FÁBREGAS, S. E., & JOHNSTONE, J. (2011). Validation of an automated wireless system to monitor sleep in healthy adults. Journal of Sleep Research, 21(2), 221-230. doi:10.1111/j.1365-2869.2011.00944.xSwarnkar, V., & Abeyratne, U. R. (2014). Bispectral analysis of single channel EEG to estimate macro-sleep-architecture. International Journal of Medical Engineering and Informatics, 6(1), 43. doi:10.1504/ijmei.2014.058531Liang, S.-F., Kuo, C.-E., Hu, Y.-H., Pan, Y.-H., & Wang, Y.-H. (2012). Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models. IEEE Transactions on Instrumentation and Measurement, 61(6), 1649-1657. doi:10.1109/tim.2012.2187242Weiss, B., Clemens, Z., Bódizs, R., & Halász, P. (2011). Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages. Brain Research Bulletin, 84(6), 359-375. doi:10.1016/j.brainresbull.2010.12.005Šušmáková, K., & Krakovská, A. (2008). Discrimination ability of individual measures used in sleep stages classification. Artificial Intelligence in Medicine, 44(3), 261-277. doi:10.1016/j.artmed.2008.07.005Olbrich, E., Achermann, P., & Wennekers, T. (2011). The sleeping brain as a complex system. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1952), 3697-3707. doi:10.1098/rsta.2011.0199Buckelmüller, J., Landolt, H.-P., Stassen, H. H., & Achermann, P. (2006). Trait-like individual differences in the human sleep electroencephalogram. Neuroscience, 138(1), 351-356. doi:10.1016/j.neuroscience.2005.11.005Van Dongen, H. P. A., Vitellaro, K. M., & Dinges, D. F. (2005). Individual Differences in Adult Human Sleep and Wakefulness: Leitmotif for a Research Agenda. Sleep, 28(4), 479-498. doi:10.1093/sleep/28.4.479A digital telemetry system for ambulatory sleep recordinghttp://physionet.mit.edu/pn4/sleep-edfx/Papers/1993-Kemp—telemetry.pdfDijk, D. J., Beersma, D. G. M., Daan, S., & van den Hoofdakker, R. H. (1989). Effects of seganserin, a 5-HT2 antagonist, and temazepam on human sleepsstages and EEG power spectra. European Journal of Pharmacology, 171(2-3), 207-218. doi:10.1016/0014-2999(89)90109-xZhang, Z., Chen, Z., Zhou, Y., Du, S., Zhang, Y., Mei, T., & Tian, X. (2014). Construction of rules for seizure prediction based on approximate entropy. Clinical Neurophysiology, 125(10), 1959-1966. doi:10.1016/j.clinph.2014.02.017Khan, J., Mariappan, R., & Venkatraghavan, L. (2014). Entropy as an indicator of cerebral perfusion in patients with increased intracranial pressure. Journal of Anaesthesiology Clinical Pharmacology, 30(3), 409. doi:10.4103/0970-9185.13728

Medidas de entropía en el procesado de señales biológicas: robustez y caracterización frente a pérdida de muestras y longitud de los registros

Las medidas de complejidad son un conjunto de métodos estadísticos que permiten estimar la regularidad de un sistema. Estos métodos se basan en técnicas de análisis no lineal de forma que se pueda caracterizar un señal sin hacer asunciones implícitas de estacionariedad o ergocidad de la misma. Estos métodos se están aplicando ampliamente sobre señales biológicas debido a la naturaleza de las mismas. Las señales biológicas se caracterizan por ser irregulares, no lineales y variables en el tiempo, de forma que los métodos tradicionales de análisis lineal no consiguen caracterizar su comportamiento completamente. Estas medidas funcionan muy bien en la práctica, ya que consiguen extraer información de las señales que de otra forma no es posible. Entre otras capacidades, consiguen diferenciar estados patológicos, precedir la aparición de un ataque epiléptico o distinguir entre estados del sueño. Pero su aplicación presenta cierta controversia, ya que carecen de una caracterización que indique al usuario qué medida aplicar en función de las características del registro, cómo debe ser aplicada o incluso c´omo interpretar los resultados obtenidos. En este trabajo se ha propuesto abordar una caracterización de algunas de las medidas de complejidad de uso más común. Se muestra una caracterización de la entropía aproximada (ApEn), la entropía muestral (SampEn), la entropía en multiples escalas o multiescala (MSE), el análisis de fluctuaciones sin tendencias (DFA), la entropía cuadrada de R´enyi (RSE) y el coeficiente de entropía muestral (CosEn), ante situaciones en las que las señales han perdido muestras o cuya longitud es limitada. La pérdida de muestras es algo muy común en la actualidad, dónde la mayoría de los registros se hacen de forma ambulatoria y el espacio de almacenamiento es limitado (compresión de datos) o la transmisión se hace de forma inalámbrica, donde el canal puede presentar condiciones inestables o interferencias que causen la pérdida de muestras, bien de forma uniforme o aleatoria. La longitud limitada de los registros puede deberse, entre otras posibilidades, a que la toma de datos se ha realizado de forma manual o ´esta resulta incómoda para el paciente. Se presenta una caracterización paramétrica de las medidas para las señales de longitud reducidas y se proponen dos métodos de optimización no supervisada para el análisis de registros de corta duración con RSE o CosEn. Este trabajo muestra cómo las medidas de entropía consideradas, presentan un comportamiento similar ante una misma situación, conservando las capacidad de separabilidad entre clases, indepedientemente del registro biológico analizado, siempre y cuando la medida se use de forma correcta. SampEn se ha erigido como la medida más estable y de mayor aplicabilidad en registros de duración media (300<N <5000) cuando las señales pierden muestras, tanto de forma aleatoria, como uniforme manteniendo coeficientes de correlación cruzados por encima de 0.8 hasta un 70% de pérdidas. Si las señales presentan desviaciones estándar altas o gran variabilidad, se recomienda la aplicación de MSE ya que introduce un suavizado y decorrelaci´on de los patrones. En señales de corta duración (100 < N < 300) se recomienda el uso de DFA, ya que permite una caracterización de la complejidad de forma estable y robusta aunque con un coste computacional alto y la necesidad de realizar una inspección visual para determinar el número de coeficientes de escalado necesarios. Finalmente, en señales de muy corta duración (N < 100) se recomienda el uso de CosEn. Se han conseguido segmentar señales de HTA en humanos de apenas 55 muestras, algo muy novedoso, con mejores estadísticos que RSE.Cirugeda Roldán, EM. (2014). Medidas de entropía en el procesado de señales biológicas: robustez y caracterización frente a pérdida de muestras y longitud de los registros [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39343TESI

What can biosignal entropy tell us about health and disease? Applications in some clinical fields

Many physiological systems are paradigmatic examples of complex networks, displaying behaviors best studied by means of tools derived from nonlinear dynamics and fractal geometry. Furthermore, while conventional wisdom considers health as an "orderly" situation (and diseases are often called "disorders"), truth is that health is characterized by a remarkable (pseudo)-randomness, and the loss of this pseudo-randomness (i.e., the "decomplexification" of the system's output) is one of the earliest sign of the system's dysfunction. The potential clinical uses of this information are evident. However, the instruments used to assess complexity are still under debate, and these tools are just beginning to find their place at the bedside. We present a brief overview of the potential uses of complexity analysis in several areas of clinical medicine. We comment on the metrics most frequently used, and we review specifically their application on certain neurologic diseases, aging, diabetes, febrile diseases and the critically ill patient.Vargas, B.; Cuesta Frau, D.; Ruiz Esteban, R.; Cirugeda Roldán, EM.; Varela, M. (2015). What can biosignal entropy tell us about health and disease? Applications in some clinical fields. Nonlinear Dynamisc, Psychology and Life Sciences. 19(4):419-436. http://hdl.handle.net/10251/61874S41943619

Glucose series complexity in hypertensive patients

Nonlinear methods have been applied to the analysis of biological signals. Complexity analysis of glucose time series may be a useful tool for the study of the initial phases of glucoregulatory dysfunction. This observational, cross-sectional study was performed in patients with essential hypertension. Glucose complexity was measured with detrended fluctuation analysis (DFA), and glucose variability was measured by the mean amplitudes of glycemic excursion (MAGE). We included 91 patients with a mean age of 59 ± 10 years. We found significant correlations for the number of metabolic syndrome (MS)-defining criteria with DFA (r = 0.233, P = .026) and MAGE (r = 0.396, P < .0001). DFA differed significantly between patients who complied with MS and those who did not (1.44 vs. 1.39, P = .018). The MAGE (f = 5.3, P = .006), diastolic blood pressures (f = 4.1, P = .018), and homeostasis model assessment indices (f = 4.2, P = .018) differed between the DFA tertiles. Multivariate analysis revealed that the only independent determinants of the DFA values were MAGE (β coefficient = 0.002, 95% confidence interval: 0.001-0.004, P = .001) and abdominal circumference (β coefficient = 0.002, 95% confidence interval: 0.000015-0.004, P = .048). In our population, DFA was associated with MS and a number of MS criteria. Complexity analysis seemed to be capable of detecting differences in variables that are arguably related to the risk of the development of type 2 diabetes.Sin financiación2.606 JCR (2014) Q3, 31/60 Peripheral Vascular DiseaseUE

A new algorithm for quadratic sample entropy optimization for very short biomedical signals: Application to blood pressure records

[EN] This paper describes a new method to optimize the computation of the quadratic sample entropy (QSE) metric. The objective is to enhance its segmentation capability between pathological and healthy subjects for short and unevenly sampled biomedical records, like those obtained using ambulatory blood pressure monitoring (ABPM). In ABPM, blood pressure is measured every 20-30 min during 24h while patients undergo normal daily activities. ABPM is indicated for a number of applications such as white-coat, suspected, borderline, or masked hypertension. Hypertension is a very important clinical issue that can lead to serious health implications, and therefore its identification and characterization is of paramount importance. Nonlinear processing of signals by means of entropy calculation algorithms has been used in many medical applications to distinguish among signal classes. However, most of these methods do not perform well if the records are not long enough and/or not uniformly sampled. That is the case for ABPM records. These signals are extremely short and scattered with outliers or missing/resampled data. This is why ABPM Blood pressure signal screening using nonlinear methods is a quite unexplored field. We propose an additional stage for the computation of QSE independently of its parameter r and the input signal length. This enabled us to apply a segmentation process to ABPM records successfully. The experimental dataset consisted of 61 blood pressure data records of control and pathological subjects with only 52 samples per time series. The entropy estimation values obtained led to the segmentation of the two groups, while other standard nonlinear methods failed. (C) 2014 Elsevier Ireland Ltd. All rights reserved.This work has been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222.Cirugeda Roldán, EM.; Cuesta Frau, D.; Miró Martínez, P.; Oltra Crespo, S.; Vigil-Medina, L.; Varela-Entrecanales, M. (2014). A new algorithm for quadratic sample entropy optimization for very short biomedical signals: Application to blood pressure records. Computer Methods and Programs in Biomedicine. 114(3):231-239. https://doi.org/10.1016/j.cmpb.2014.02.008S231239114