Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation

[EN] Most cardiac arrhythmias can be classified as atrial flutter, focal atrial tachycardia, or atrial fibrillation. They have been usually treated using drugs, but catheter ablation has proven more effective. This is an invasive method devised to destroy the heart tissue that disturbs correct heart rhythm. In order to accurately localise the focus of this disturbance, the acquisition and processing of atrial electrograms form the usual mapping technique. They can be single potentials, double potentials, or complex fractionated atrial electrogram (CFAE) potentials, and last ones are the most effective targets for ablation. The electrophysiological substrate is then localised by a suitable signal processing method. Sample Entropy is a statistic scarcely applied to electrograms but can arguably become a powerful tool to analyse these time series, supported by its results in other similar biomedical applications. However, the lack of an analysis of its dependence on the perturbations usually found in electrogram data, such as missing samples or spikes, is even more marked. This paper applied SampEn to the segmentation between non-CFAE and CFAE records and assessed its class segmentation power loss at different levels of these perturbations. The results confirmed that SampEn was able to significantly distinguish between non-CFAE and CFAE records, even under very unfavourable conditions, such as 50% of missing data or 10% of spikes.This research was supported by Research Center for Informatics (no. CZ.02.1.01/0.0/0.0/16-019/0000765).Cirugeda Roldan, EM.; Molina Picó, A.; Novák, D.; Cuesta Frau, D.; Kremen, V. (2018). Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2018/1874651SAhmed, S., Claughton, A., & Gould, P. A. (2015). Atrial Flutter — Diagnosis, Management and Treatment. Abnormal Heart Rhythms. doi:10.5772/60700Kirchhof, P., & Calkins, H. (2016). Catheter ablation in patients with persistent atrial fibrillation. European Heart Journal, 38(1), 20-26. doi:10.1093/eurheartj/ehw260Nademanee, K., Lockwood, E., Oketani, N., & Gidney, B. (2010). Catheter ablation of atrial fibrillation guided by complex fractionated atrial electrogram mapping of atrial fibrillation substrate. Journal of Cardiology, 55(1), 1-12. doi:10.1016/j.jjcc.2009.11.002NG, 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.xKottkamp, H., & Hindricks, G. (2007). Complex fractionated atrial electrograms in atrial fibrillation: A promising target for ablation, but why, when, and how? Heart Rhythm, 4(8), 1021-1023. doi:10.1016/j.hrthm.2007.05.011Křemen, 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:10.1088/0967-3334/29/12/002Nademanee, 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.2003.12.054Scherr, D., Dalal, D., Cheema, A., Cheng, A., Henrikson, C. A., Spragg, D., … Dong, J. (2007). Automated detection and characterization of complex fractionated atrial electrograms in human left atrium during atrial fibrillation. Heart Rhythm, 4(8), 1013-1020. doi:10.1016/j.hrthm.2007.04.021Almeida, T. P., Chu, G. S., Salinet, J. L., Vanheusden, F. J., Li, X., Tuan, J. H., … Schlindwein, F. S. (2016). Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation. Medical & Biological Engineering & Computing, 54(11), 1695-1706. doi:10.1007/s11517-016-1456-2Molina-Picó, A., Cuesta-Frau, D., Aboy, M., Crespo, C., Miró-Martínez, P., & Oltra-Crespo, S. (2011). Comparative study of approximate entropy and sample entropy robustness to spikes. Artificial Intelligence in Medicine, 53(2), 97-106. doi:10.1016/j.artmed.2011.06.007Cuesta–Frau, D., Miró–Martínez, P., Jordán Núñez, J., Oltra–Crespo, S., & Molina Picó, A. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine, 87, 141-151. doi:10.1016/j.compbiomed.2017.05.028Demont-Guignard, S., Benquet, P., Gerber, U., & Wendling, F. (2009). Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model. IEEE Transactions on Biomedical Engineering, 56(12), 2782-2795. doi:10.1109/tbme.2009.2028015Molina–Picó, A., Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., & Aboy, M. (2013). Influence of QRS complex detection errors on entropy algorithms. Application to heart rate variability discrimination. Computer Methods and Programs in Biomedicine, 110(1), 2-11. doi:10.1016/j.cmpb.2012.10.014Ganesan, P., Cherry, E. M., Pertsov, A. M., & Ghoraani, B. (2015). Characterization of Electrograms from Multipolar Diagnostic Catheters during Atrial Fibrillation. BioMed Research International, 2015, 1-9. doi:10.1155/2015/272954Lake, 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.00069.2002Kim, K. K., Baek, H. J., Lim, Y. G., & Park, K. S. (2012). Effect of missing RR-interval data on nonlinear heart rate variability analysis. Computer Methods and Programs in Biomedicine, 106(3), 210-218. doi:10.1016/j.cmpb.2010.11.011Richman, 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.h2039Cirugeda–Roldán, E., 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(12), 7493-7509. doi:10.3390/e17117493PORTER, 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.xKonings, 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.89.4.1665Fay, M. P., & Proschan, M. A. (2010). Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics Surveys, 4(0), 1-39. doi:10.1214/09-ss051Richman, J. S. (2007). Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics - Theory and Methods, 36(5), 1005-1019. doi:10.1080/03610920601036481Pincus, 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/bf01619355Alcaraz, R., & Rieta, J. J. (2009). Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation. Computer Methods and Programs in Biomedicine, 93(2), 148-154. doi:10.1016/j.cmpb.2008.09.001Alcaraz, 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.2010.02.009Costa, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale Entropy Analysis of Complex Physiologic Time Series. Physical Review Letters, 89(6). doi:10.1103/physrevlett.89.06810

Práctica de Aprendizaje Profundo en la Asignatura de Tecnología para Sistemas Inteligentes

[ES] En este trabajo se presentan los resultados de una práctica llevada a cabo por los alumnos de la asignatura Tecnología para Sistemas Inteligentes, impartida en el Grado de Ingeniería Informática (Universitat Politècnica de València, Campus d¿Alcoi). La práctica desarrolla una aplicación de reconocimiento de voz en el que un sistema Arduino se entrena para que pueda discriminar entre unos pocos comandos y actuar en consecuencia. Los alumnos tienen que grabar los ficheros con su propia voz para entrenar el modelo y compartir entre ellos las muestras con la intención de mantener una base de datos lo suficientemente representativa.Molina Picó, A.; Jordán-Núñez, J.; Micó-Vicent, B. (2021). Práctica de Aprendizaje Profundo en la Asignatura de Tecnología para Sistemas Inteligentes. Compobell. 29-32. http://hdl.handle.net/10251/191320293

Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics

[EN] This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.Cuesta Frau, D.; Miró Martínez, P.; Jordán Núñez, J.; Oltra Crespo, S.; Molina Picó, A. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine. 87:141-151. doi:10.1016/j.compbiomed.2017.05.028S1411518

Forest monitoring and wildland early fire detection by a hierarchical wireless sensor network

A wildland fire is an uncontrolled fire that occurs mainly in forest areas, although it can also invade urban or agricultural areas. Among the main causes of wildfires, human factors, either intentional or accidental, are the most usual ones. The number and impact of forest fires are expected to grow as a consequence of the global warming. In order to fight against these disasters, it is necessary to adopt a comprehensive, multifaceted approach that enables a continuous situational awareness and instant responsiveness. This paper describes a hierarchical wireless sensor network aimed at early fire detection in risky areas, integrated with the fire fighting command centres, geographical information systems, and fire simulators. This configuration has been successfully tested in two fire simulations involving all the key players in fire fighting operations: fire brigades, communication systems, and aerial, coordination, and land means.This work has been developed under the framework of the research project PROMETEO, CEN-20101010, funded by the Centre for the Technological Industrial Development (CDTI), Spanish Ministerio de Economia y Competitividad.Molina Picó, A.; Cuesta Frau, D.; Araujo, Á.; Alejandre, J.; Rozas, A. (2016). Forest monitoring and wildland early fire detection by a hierarchical wireless sensor network. Journal of Sensors. (8325845):1-8. https://doi.org/10.1155/2016/8325845S18832584

i-Light - Intelligent Luminaire Based Platform for Home Monitoring and Assisted Living

[EN] We present i-Light, a cyber-physical platform that aims to help older adults to live safely within their own homes. The system is the result of an international research project funded by the European Union and is comprised of a custom developed wireless sensor network together with software services that provide continuous monitoring, reporting and real-time alerting capabilities. The principal innovation proposed within the project regards implementation of the hardware components in the form of intelligent luminaires with inbuilt sensing and communication capabilities. Custom luminaires provide indoor localisation and environment sensing, are cost-effective and are designed to replace the lighting infrastructure of the deployment location without prior mapping or fingerprinting. We evaluate the system within a home and show that it achieves localisation accuracy sufficient for room-level detection. We present the communication infrastructure, and detail how the software services can be configured and used for visualisation, reporting and real-time alerting.This work was funded by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number 46E/2015, i-Light-A pervasive home monitoring system based on intelligent luminaires.Marin, I.; Vasilateanu, A.; Molnar, A.; Bocicor, MI.; Cuesta Frau, D.; Molina Picó, A.; Goga, N. (2018). i-Light - Intelligent Luminaire Based Platform for Home Monitoring and Assisted Living. Electronics. 7(10):1-24. https://doi.org/10.3390/electronics7100220S124710World Report on Ageing and Health http://apps.who.int/iris/bitstream/10665/186463/1/9789240694811_eng.pdf?ua=1ECP Makes Switching to eMAR Easy http://extendedcarepro.com/products/Carevium Assisted Living Software http://www.carevium.com/carevium-assisted-living-software/Yardi EHR http://www.yardi.com/products/ehr-senior-care/Yardi eMAR http://www.yardi.com/products/emar/Botia, J. A., Villa, A., & Palma, J. (2012). Ambient Assisted Living system for in-home monitoring of healthy independent elders. Expert Systems with Applications, 39(9), 8136-8148. doi:10.1016/j.eswa.2012.01.153Lopez-Guede, J. M., Moreno-Fernandez-de-Leceta, A., Martinez-Garcia, A., & Graña, M. (2015). Lynx: Automatic Elderly Behavior Prediction in Home Telecare. BioMed Research International, 2015, 1-18. doi:10.1155/2015/201939Luca, S., Karsmakers, P., Cuppens, K., Croonenborghs, T., Van de Vel, A., Ceulemans, B., … Vanrumste, B. (2014). Detecting rare events using extreme value statistics applied to epileptic convulsions in children. Artificial Intelligence in Medicine, 60(2), 89-96. doi:10.1016/j.artmed.2013.11.007Better Health Assessments Every Day, for Better Everyday Living http://healthsense.com/Home Telehealth https://www.usa.philips.com/healthcare/solutions/enterprise-telehealth/home-telehealthThe Carelink Network http://www.medtronic.com/us-en/healthcare-professionals/products/cardiac-rhythm/managing-patients/information-systems/carelink-network.htmlHaigh, P. A., Bausi, F., Ghassemlooy, Z., Papakonstantinou, I., Le Minh, H., Fléchon, C., & Cacialli, F. (2014). Visible light communications: real time 10 Mb/s link with a low bandwidth polymer light-emitting diode. Optics Express, 22(3), 2830. doi:10.1364/oe.22.002830Indoor Positioning System http://www.gelighting.com/LightingWeb/na/solutions/control-systems/indoor-positioning-system.jspIndoor and Outdoor Lighting Solutions http://www.acuitybrands.com/solutions/featured-spacesHuang, C.-N., & Chan, C.-T. (2011). ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI. Procedia Computer Science, 5, 58-65. doi:10.1016/j.procs.2011.07.010Charlon, Y., Fourty, N., & Campo, E. (2013). A Telemetry System Embedded in Clothes for Indoor Localization and Elderly Health Monitoring. Sensors, 13(9), 11728-11749. doi:10.3390/s130911728Patient/Elderly Activity Monitoring Using WiFi-Based Indoor Localization https://wiki.cc.gatech.edu/designcomp/images/3/3d/HHH_Report.pdfReal Time Location System http://zonith.com/products/rtls/Accurate Positioning https://www.pozyx.io/yooBee System Overview https://www.blooloc.com/over-yoobeeThe Top Indoor Location Engine for Smart Apps https://senion.com/Locating People, Way-Finding, and Attendance Tracking https://estimote.com/products/Indoor Navigation, Indoor Positioning, Indoor Analytics and Indoor Tracking https://www.infsoft.com/Lighting Reimagined https://www.lifx.com/Tabu. Lumen. Simply Brighter http://www.lumenbulb.net/Philips Hue http://www2.meethue.com/en-usElgato Avea https://www.elgato.com/en/aveaiLumi—The World’s Most Intelligent Light Bulbs hhttps://www.indiegogo.com/projects/ilumi-the-world-s-most-intelligent-light-bulbs--5#/Bluegiga BLE112 Bluetooth® Smart Module http://www.silabs.com/products/wireless/bluetooth/bluetooth-low-energy-modules/ble112-bluetooth-smart-moduleISO/IEEE 11073 https://www.iso.org/standard/67821.htmlDescription https://www.diodes.com/assets/Datasheets/ZXLD1366.pdfDigital Humidity Sensor SHT2x https://www.sensirion.com/en/environmental-sensors/humidity-sensors/humidity-temperature-sensor-sht2x-digital-i2c-accurate/Photo IC Type High Sensitive Light Sensor https://industrial.panasonic.com/cdbs/www-data/pdf/ADD8000/ADD8000CE2.pdfWSP2110 VOC Gas Sensor http://www.winsen-sensor.com/products/flat-surfaced-gas-sensor/wsp2110.htmlLow Power-Consumption CO2 Sensor http://www.winsen-sensor.com/d/files/PDF/Solid%20Electrolyte%20CO2%20Sensor/MG812%20CO2%20Manual%20V1.1.pdfGP2Y1010AU0F Compact Optical Dust Sensor http://www.sharp-world.com/products/device/lineup/data/pdf/datasheet/gp2y1010au_e.pdfEKMC (VZ) Series http://www3.panasonic.biz/ac/e/control/sensor/human/vz/index.jspSensors for Automotive & Industrial Applications: Grid-EYE Infrared Array Sensor https://na.industrial.panasonic.com/products/sensors/sensors-automotive-industrial-applications/grid-eye-infrared-array-sensorGeneric Attributes https://www.bluetooth.com/specifications/gattDeveloping NFC Applications. (2011). Near Field Communication, 151-239. doi:10.1002/9781119965794.ch5Matsuoka, H., Wang, J., Jing, L., Zhou, Y., Wu, Y., & Cheng, Z. (2014). Development of a control system for home appliances based on BLE technique. 2014 IEEE International Symposium on Independent Computing (ISIC). doi:10.1109/indcomp.2014.7011751Standard ECMA-404. The JSON Data Interchange Format http://www.ecma-international.org/publications/files/ECMA-ST/ECMA-404.pdfThe EU General Data Protection Regulation http://www.eugdpr.org/Tews, E., & Beck, M. (2009). Practical attacks against WEP and WPA. Proceedings of the second ACM conference on Wireless network security - WiSec ’09. doi:10.1145/1514274.1514286Farooq, U., & Aslam, M. F. (2017). Comparative analysis of different AES implementation techniques for efficient resource usage and better performance of an FPGA. 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Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

[EN] This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.The Czech partners were supported by DROIKEM000023001 and RVOVFN64165. No funding was received to support this research work by the Spanish partners.Cuesta Frau, D.; Novák, D.; Burda, V.; Molina Picó, A.; Vargas-Rojo, B.; Mraz, M.; Kavalkova, P.... (2018). Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy. 20(11):1-18. https://doi.org/10.3390/e20110871S118201

Evaluación de la competencia transversal “Responsabilidad ética, medioambiental y profesional” a través de una e-rúbrica en el laboratorio

[ES] El proceso de convergencia hacia el Espacio Europeo de Enseñanza Superior ha puesto de relieve la importancia del dominio de competencias transversales (CTs) durante la formación universitaria. Dichas competencias confieren al estudiante la capacidad de innovación y de adaptación a los cambios, siendo su adquisición necesaria para la vida profesional. En la Universidad Politécnica de Valencia, se han redactado 13 CTs que aúnan las competencias de la normativa vigente y las de las agencias de acreditación. En nuestro grupo de innovación educativa estudiamos diferentes métodos de enseñanza-aprendizaje y evaluación de las competencias transversales en asignaturas relacionadas con las ciencias de la vida. En concreto, en este trabajo presentamos una propuesta para evaluar la CT “Responsabilidad ética, medioambiental y profesional”. Esta competencia pretende la obtención de conocimientos, habilidades, destrezas y actitudes útiles para interactuar con el entorno, de forma ética, responsable y sostenible, ante uno mismo y los demás. Las asignaturas relacionadas con las ciencias de la vida y, en particular, sus créditos de laboratorio, resultan un marco idóneo para la adquisición de dicha competencia. Nuestra propuesta de evaluación de la misma se basa en una rúbrica que ha de ser cumplimentada por los pares a través de una aplicación telemática.Este trabajo ha sido financiado por un Proyecto de Innovación y Mejora Educativa concedido por el Vicerrectorado de Estudios, Calidad y Acreditación de la Universitat Politècnica de València.Bañuls Polo, M.; López Gresa, MP.; Cebolla Cornejo, J.; Díez Niclós, MJTDJ.; Esteras Gómez, C.; Ferriol Molina, M.; González Martínez, MÁ.... (2015). Evaluación de la competencia transversal “Responsabilidad ética, medioambiental y profesional” a través de una e-rúbrica en el laboratorio. En In-Red 2015 - CONGRESO NACIONAL DE INNOVACIÓN EDUCATIVA Y DE DOCENCIA EN RED. Editorial Universitat Politècnica de València. https://doi.org/10.4995/INRED2015.2015.154

Caracterización de medidas de regularidad en señales biomédicas. Robustez a outliers

Los sistemas fisiológicos generan señales eléctricas durante su funcionamiento. Estas señales pueden ser registradas y representadas, constituyendo un elemento fundamental de ayuda al diagnóstico en la práctica clínica actual. Sin embargo, la inspección visual no permite la extracción completa de la información contenida en estas señales. Entre las técnicas de procesamiento automático, destacan los métodos no lineales, específicamente aquellos relacionados con la estimación de la regularidad de la señal subyacente. Estos métodos están ofreciendo en los ´últimos años resultados muy significativos en este ´ámbito. Sin embargo, son muy sensibles a las interferencias en las señales, ocurriendo una degradación significativa de su capacidad diagnostica si las señales biomédicas están contaminadas. Uno de los elementos que se presenta con cierta frecuencia en los registros fisiológicos y que contribuye a esta degradación de prestaciones en estimadores no lineales, son los impulsos de cortad duración, conocidos en este contexto como spikes. En este trabajo se pretende abordar la problemática asociada a la presencia de spikes en bioseñales, caracterizando su influencia en una serie de medidas concretas, para que la posible degradación pueda ser anticipada y las contramedidas pertinentes aplicadas. En concreto, las medidas de regularidad caracterizadas son: Approximate Entropy (ApEn), Sample Entropy (SampEn), Lempel Ziv Complexity (LZC) y Detrended Fluctuation Analysis (DFA). Todos estos métodos han ofrecido resultados satisfactorios en multitud de estudios previos en el procesado de señales biomédicas. La caracterización se lleva a cabo mediante un exhaustivo estudio experimental en el cual se aplican spikes controlados a diferentes registros fisiológicos, y se analiza cuantitativa y cualitativamente la influencia de dichos spikes en la estimación resultante. Los resultados demuestran que el nivel de interferencia, así como los parámetros de las medidas de regularidad, afectan de forma muy variada. En general, LZC es la medida más robusta del conjunto caracterizado frente a spikes, mientras que DFA es la más vulnerable. Sin embargo, la capacidad de discernir entre clases permanece en muchos casos, a pesar de los cambios producidos en los valores absolutos de entropía.Molina Picó, A. (2014). Caracterización de medidas de regularidad en señales biomédicas. Robustez a outliers [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39346TESI

Wireless Message Center

Segarra Abad, M.; Molina Picó, A. (2012). Wireless Message Center. http://hdl.handle.net/10251/35946.Archivo delegad

Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy

Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear I R n mapping into I R , where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures