Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapie

Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction

[ES] Este trabajo propone la detección de FV y su discriminación de TV y otros ritmos cardiacos basándose en la representación tiempo-frecuencia del ECG y su conversión en imágen como entrada a un clasificador de vecinos más cercanos (KNN) sin necesidad de extracción de parámetros adicionales. Tres variantes de datos de entrada al clasificador son evaluados. Los resultados clasifican la señal en cuatro clases diferentes: ’Normal’ para latidos con ritmo sinusal, ’FV’ para fibrilación ventricular, ’TV’ para taquicardia ventricular y ’Otros’ para el resto de ritmos. Los resultados para detección de FV mostraron 88,27% de sensibilidad y 98,22% de especificidad para la entrada de imágen equivalente reducida que es la más rápida computacionalmente a pesar de obtener resultados de clasificación ligeramente inferiores a las representaciones no reducidas. En el caso de TV, se alcanzó un 88,31% de sensibilidad y 98,80% de especificidad, un 98,14% de sensibilidad y 96,82% de especificidad para ritmo sinusal normal y 96,91% de sensibilidad con 99,06% de especificidad para la clase ’Otros’. Finalmente, se realiza una comparación con otros algoritmos.[EN] This work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours - KNN) without any further signal parameter extraction or additional characteristics. For comparison purposes, three time-frequency variants are analysed: pseudo Wigner-Ville representation (RTF), grey-scale image obtained from RTF (IRTF), and reduced image from IRTF (reduced IRTF). Four types of rhythms (classes) are defined: ’Normal’ for sinus rhythm, ’VT’ for ventricular tachycardia, ’VF’ for ventricular fibrillation and ’Others’ for the rest of rhythms. Classification results for VF detection in case of reduced IRTF are 88.27% sensitivity and 98.22% specificity. In case of VT, 88.31% sensitivity and 98.80% specificity is obtained, 98.14% sensitivity and 96.82% specificity for normal rhythms, and 96.91% sensitivity and 99.06% specificity for other rhythms. Finally, results are compared with other authors.Mjahad, A.; Rosado Muñoz, A.; Bataller Mompeán, M.; Francés Víllora, JV.; Guerrero Martínez, JF. (2017). Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros. Revista Iberoamericana de Automática e Informática industrial. 15(1):124-132. https://doi.org/10.4995/riai.2017.8833OJS124132151Classen, T. A. C. M., Mecklenbrauker, W. F. G., 1980. The Wigner Distribution: A Tool for Time-Frequency Signal Analysis - Part 2: Discrete-Time Signals. Philips Journal of Research 35, 276-350.Cohen, L., Jul 1989a. Time frequency distributions a review. Proceedings of the IEEE 77 (7), 941-981. https://doi.org/10.1109/5.30749Cohen, L., 1989b. Time-frequency distributions-a review. Proceeding of the IEEE 77 (7), 941-981. https://doi.org/10.1109/5.30749Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T., Ahmed, T., 2016. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine 127, 52- 63. https://doi.org/10.1016/j.cmpb.2015.12.024Hlawatsch, F., Boudreaux-Bartels, G. F., April 1992. Linear and quadratic time-frequency signal representations. IEEE Signal Processing Magazine 9 (2), 21-67. https://doi.org/10.1109/79.127284Ibaida, A., Khalil, I., Aug 2010. Distinguishing between Ventricular Tachycardia and Ventricular Fibrillation from Compressed ECG Signal in Wireless body Sensor Networks. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. pp. 2013-2016. https://doi.org/10.1109/IEMBS.2010.5627888Jekova, I., 2007a. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomedical Signal Processing and Control 2 (1), 25 - 33. https://doi.org/10.1016/j.bspc.2007.01.002Jekova, I., 2007b. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomedical Signal Processing and Control 2 (1), 25-33. https://doi.org/10.1016/j.bspc.2007.01.002Jekova, I., Krasteva, V., 2004. Real time detection of ventricular fibrillation and tachycardia. Physiological measurement 25 (5), 1167. https://doi.org/10.1088/0967-3334/25/5/007Jin, D., Dai, C., Gong, Y., Lu, Y., Zhang, L., Quan, W., Li, Y., 2017. Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis? Resuscitation 111, 48 - 54. https://doi.org/10.1016/j.resuscitation.2016.11.022Kabir, M. A., Shahnaz, C., 2012. Denoising of ecg signals based on noise reduction algorithms in emd and wavelet domain. Biomedical Signal Processing and Control l 7 (5). https://doi.org/10.1016/j.bspc.2011.11.003Kao, T.-P., Wang, J.-S., Lin, C.-W., Yang, Y.-T., Juang, F.-C., June 2012. Using bootstrap adaboost with knn for ecg-based automated obstructive sleep apnea detection. In: The 2012 International Joint Conference on Neural Networks (IJCNN). pp. 1-5. https://doi.org/10.1109/IJCNN.2012.6252716Kaur, L., Singh, V., May 2013. Ventricular Fibrillation Detection using Empirical Mode Decomposition and Approximate Entropy. International Journal of Emerging Technology and Advanced Engineering 3 (5), 260-268.Kaur, M., Singh, B., Seema, 2011. Comparison of different approaches for removal of baseline wander from ecg signal. In: Proceedings of the International Conference &Workshop on Emerging Trends in Technology. ICWET'11. ACM, New York, NY, USA, pp. 1290-1294. https://doi.org/10.1145/1980022.1980307Labatut, V., Cherifi, H., May 2011. Accuracy measures for the comparison of classifiers. In: Ali, A.-D. (Ed.), The 5th International Conference on Information Technology. Al-Zaytoonah University of Jordan, amman, Jordan, pp.1,5.Li, Q., Rajagopalan, C., Clifford, G. D., 2014. Ventricular fibrillation and tachycardia classification using a machine learning approach. Biomedical Engineering, IEEE Transactions on 61 (6), 1607-1613.Mahmoud, S. S., Hussain, Z. M., Cosic, I., Fang, Q., 2006. Time-frequency analysis of normal and abnormal biological signals. Biomedical Signal Processing and Control 1 (1), 33 - 43. https://doi.org/10.1016/j.bspc.2006.02.001Martin, W., Flandrin, P., Dec 1985. Wigner-ville spectral analysis of nonstationary processes. IEEE Transactions on Acoustics, Speech, and Signal Processing 33 (6), 1461-1470. https://doi.org/10.1109/TASSP.1985.1164760Mateo, J., Torres, A., Aparicio, A., Santos, J., 2016. An efficient method for ECG beat classification and correction of ectopic beats. Computers and Electrical Engineering 53, 219 - 229. https://doi.org/10.1016/j.compeleceng.2015.12.015Mjahad, A., Rosado-Mu-oz, A., Guerrero-Martinez, J., Bataller-Mompean, M., Frances-Villora, J. V., 2015. ECG Analysis for Ventricular Fibrillation Detection Using a Boltzmann Network. In: Braidot, A., Hadad, A. (Eds.), VI Latin American Congress on Biomedical Engineering CLAIB 2014, Parana, Argentina 29, 30, 31 October 2014. Vol. 49 of IFMBE Proceedings. Springer International Publishing, pp. 532-535. https://doi.org/10.1007/978-3-319-13117-7Murakoshi, N., Aonuma, K., 2013. Epidemiology of arrhythmias and sudden cardiac death in asia. Circulation Journal 77 (10), 2419-2431. https://doi.org/10.1253/circj.CJ-13-1129Othman, M. A., Safri, N. M., Ghani, I. A., Harun, F. K. C., Ariffin, I., 2013. A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation. Biomedical Signal Processing and Control 8 (2), 222 - 227.https://doi.org/10.1016/j.bspc.2012.10.001Phong, P. A., Thien, K. Q., Oct 2009. Classification of Cardiac Arrhythmias Using Interval Type-2 TSK Fuzzy System. In: Knowledge and Systems Engineering, 2009. KSE '09. International Conference on. pp. 1-6. https://doi.org/10.1109/KSE.2009.19Poularikas, A. D., 1999. The transforms and applications handbooks. ACRC Handbook published in cooperatio with IEEE Press, Depatment of electrical an computer engineering the univesity of Alabama in Huntsville.Rangayyan, R. M., 2002. Biomedical signal analysis: A case-study approach. In: IEEE Press Series in Biomedical Engineering.Ravindra Pratap Narwaria, S. V., Singhal, P. K., 2011. Removal of baseline wander and power line interference from ecg signal - a survey approach. International Journal of Electronics Engineering 3, 107-111.Rosado, A., Guerrero, J., Bataller, M., Chorro, J., 2001. Fast non-invasive ventricular fibrillation detection method using pseudo wigner-ville distribution. In: Computers in Cardiology 2001. pp. 237-240. https://doi.org/10.1109/CIC.2001.977635Saini, R., Bindal, N., Bansal, P., May 2015. Classification of heart diseases from ecg signals using wavelet transform and knn classifier. In: Computing, Communication Automation (ICCCA), 2015 International Conference on. pp. 1208-1215. https://doi.org/10.1109/CCAA.2015.7148561Sharma, L., Dandapat, S., Mahanta, A., 2010. Ecg signal denoising using higher order statistics in wavelet subbands. Biomedical Signal Processing and Control 5 (3), 214 - 222. https://doi.org/10.1016/j.bspc.2010.03.003Sornmo, L., Laguna, P., 2005. Bioelectrical signal processing in cardiac and neurological applications. In: Elsevier Academic Press.Tan, W., Foo, C. L., Chua, T. W., July 2007. Type-2 Fuzzy System for ECG Arrhythmic Classification. In: Fuzzy Systems Conference, 2007. FUZZIEEE 2007. IEEE International. pp. 1-6. https://doi.org/10.1109/FUZZY.2007.4295478Valenzuela, J. V., 2008. Interpolacion de Formas en Imagenes Usando Morfologia Matematica. Ph.D. thesis, Departamento de Lenguajes, Sistemas Informaticos e Ingeniera de Software Facultad de Informatica Universidad Politecnica de Madrid, Director de Tesis: Jose Crespo del Arco.Viitasalo, M., Karjalainen, J., 1992. Q T intervals at Heart rates From 50 to 120 Beats per Minute During 24 Hour Electrocardiographic Recordings in 100 Healthy Men Effects of Atenolol. American Heart Association 86 (5), 1439-1442. https://doi.org/10.1161/01.CIR.86.5.1439von Borries, R. F., Pierluissi, J. H., Nazeran, H., Jan 2005. Wavelet transformbased ecg baseline drift removal for body surface potential mapping. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. pp. 3891-3894. https://doi.org/10.1109/IEMBS.2005.1615311V.Oppenheim, A., Willsky, A. S., Nawab, S. H., 1998. Signals and systems. Prentice Hall Internationa,Inc, Massachusettes Intitue Technology with Boston Univeristy.Xia, D., Meng, Q., Chen, Y., Zhang, Z., 2014. Classification of Ventricular T achycardia and Fibrillation Based on the Lempel-Ziv Complexity and EMD 8590, 322-329. https://doi.org/10.1007/978-3-319-09330-7_39Xie, H.-B., Zhong-Mei, G., Liu, H., 2011. Classification of Ventricular Tachycardia and Fibrillation Using Fuzzy Similarity-based Approximate entropy. Expert Systems with Applications 38 (4), 3973 - 3981. https://doi.org/10.1016/j.eswa.2010.09.058Yilmaz, B., Arikan, E., Asyali, M. H., April 2010. Use of knn and quadratic discriminant analysis methods for sleep staging from single lead ecg recordings. Biomedical Engineering Meeting (BIYOMUT), 2010 15th National, 1-4. https://doi.org/10.1109/BIYOMUT.2010.5479833Yochum, M., Renaud, C., Jacquir, S., 2016. Automatic detection of p, QRS and t patterns in 12 leads ECG signal based on CWT. Biomedical Signal Processing and Control 25, 46 - 52. https://doi.org/10.1016/j.bspc.2015.10.01

Aging and cardiovascular complexity: effect of the length of RR tachograms

As we age, our hearts undergo changes that result in a reduction in complexity of physiological interactions between different control mechanisms. This results in a potential risk of cardiovascular diseases which are the number one cause of death globally. Since cardiac signals are nonstationary and nonlinear in nature, complexity measures are better suited to handle such data. In this study, three complexity measures are used, namely Lempel–Ziv complexity (LZ), Sample Entropy (SampEn) and Effort-To-Compress (ETC). We determined the minimum length of RR tachogram required for characterizing complexity of healthy young and healthy old hearts. All the three measures indicated significantly lower complexity values for older subjects than younger ones. However, the minimum length of heart-beat interval data needed differs for the three measures, with LZ and ETC needing as low as 10 samples, whereas SampEn requires at least 80 samples. Our study indicates that complexity measures such as LZ and ETC are good candidates for the analysis of cardiovascular dynamics since they are able to work with very short RR tachograms

Análisis de señales biomédicas para aplicación de terapias en la fibrilación ventricular cardiaca

La muerte súbita es una muerte natural, inesperada y rápida en un tiempo límite de 24 horas después del comienzo de un proceso patológico. Las causas más comunes de la muerte súbita son las enfermedades Cardiovasculares (ECV) que resultan estar entre las principales causas de muerte en todo el mundo. En 2012, la Organización Mundial de la Salud (OMS) registró 17,5 millones de muertes por ECV, que representan el 31 % de todas las muertes registradas en el mundo . Una de las enfermedades cardiovasculares con mayor mortalidad es la Fibrilación Ventricular (FV), que es una arritmia cardíaca producida por una actividad eléctrica desorganizada del corazón. Durante la FV, los ventrículos se contraen de forma asíncrona con ausencia de latido efectivo, fallando el bombeo sanguíneo, lo que produce la muerte súbita en el paciente, si no se trata de forma adecuada en un plazo de pocos minutos. Las desfibrilación es el único tratamiento definitivo posible de la FV, que consiste en la aplicación de un choque eléctrico de alta energía sobre el pecho del paciente, facilitando así el reinicio de la actividad eléctrica cardíaca normal. El éxito de la desfibrilación es inversamente proporcional al intervalo de tiempo desde el comienzo del episodio hasta la aplicación de la descarga. Aparecen muchas dificultades a la hora de diagnosticar la FV: por una parte están las características intrínsecas de la FV (falta de organización, irregularidad, etc.), y por otra, la gran similitud entre la FV y otras patologías cardíacas entre ellas la Taquicardia Ventricular (TV). La diferenciación entre la TV y la FV es bastante complicada: el diagnóstico de la TV como FV en un paciente puede ocasionarle graves lesiones a la hora de aplicarle la terapia correspondiente a FV (descarga eléctrica de alto voltaje), es más, puede causarle una FV. Por el contrario, si la FV se interpreta incorrectamente como TV, el resultado puede ser también peligroso para la vida. Por lo tanto, un método de detección eficaz para distinguir FV de TV tiene mucha importancia en la investigación clínica. Con el fin de diagnosticar y tratar las enfermedades cardiovasculares (FV por ejemplo), se establecen dos grandes grupos de métodos diagnósticos en cardiología: métodos diagnósticos invasivos y no invasivos. Las técnicas invasivas requieren introducir catéteres en el organismo, con el objetivo de medir presiones de las cavidades cardíacas, para registrar la actividad eléctrica. Las técnicas no invasivas están enfocadas a caracterizar el estado fisiopatológico del corazón a través de electrodos colocados directamente sobre la piel del paciente. El electrocardiograma (ECG) es un examen no invasivo, de bajo costo, que se ha utilizado como el método básico de diagnóstico de desórdenes cardíacos de conducción eléctrica, mediante el estudio de la frecuencia cardíaca y la morfología de diferentes ondas que constituyen el ciclo cardíaco. El análisis ECG constituye una buena fuente de información a partir de la cual se pueden detectar diferentes tipos de enfermedades cardíacas. Debido a que la señal de ECG es una señal aleatoria no estacionaria, el análisis en el dominio del tiempo no resulta ser suficientemente sensible a las distorsiones de las formas de la onda ECG. Sin embargo estos métodos no siempre presentan todas las informaciones que pueden ser extraídas de las señales ECG, con lo cual, se pierde la información sobre la frecuencia, la cual muestra una información más adicional de la señal. El diagnóstico en el dominio de la frecuencia utiliza métodos como la transformada de Fourier. Por lo tanto, el análisis en el dominio de la frecuencia permite determinar las frecuencias de la señal. Por otro lado, se pierde la información de tipo temporal de la señal, con lo cual es un método muy limitado y no es útil para el análisis de señales no estacionarias. Varios estudios han utilizado modelos matemáticos que combinan la información temporal y espectral en la misma representación. Esta técnica Representación Tiempo-Frecuencia (RTF) es muy importante en el tratamiento de las señales no estacionarias como la señal de ECG, ya que distribuye la energía de la señal en el espacio bidimensional tiempo-frecuencia. Además, múltiples factores alteran la adquisición y registro de la señal ECG: por un lado está la influencia del medio ambiente (la interferencia de red 50-60 Hz, la línea base, etc.), y por otro lado están las perturbaciones de origen fisiológico como los de la electromiografía (EMG). La reducción del ruido en el ECG ha sido uno de los principales campos de investigación en las últimas décadas, ya que una adecuada reducción permite realizar un buen pre-procesado de la señal, extrayendo de ésta la máxima cantidad de información posible y eliminando la no deseable. El uso de imágenes de representación de tiempo-frecuencia (t-f) como la entrada directa al clasificador es lo novedoso de esta tesis doctoral. Se plantea la hipótesis de que este método facilita mejorar los resultados de la clasificación ya que permite eliminar la extracción de características típicas, y su correspondiente pérdida de información, que además se utilizan para la evaluación y la comparación con otros autores. Materiales y Métodos: se ha utilizado las bases de datos estándar del MIT-BIH Malignant Ventricular Arrhytmia Data base y AHA (2000 series) para obtener los registros de las señales ECG, creando a partir de ellos un conjunto de entrenamiento y uno de prueba para los algoritmos de clasificación empleados. Un total de 24 registros de monitorización continua (22 registros de MIT-BIH más dos adicionales de la base de datos AHA) se ha empleado, con frecuencia de muestreo de 125 Hz. Como método se ha implementado un algoritmo de filtro, con el fin de reducir la línea base, se desarrolla un enventanado que indica el comienzo de la Ventana de tiempo (Vt) de la señal del ECG. A cada ventana obtenida se le aplicó la Transformada de Hilbert (TH), después se le aplicó la RTF como entrada a los cinco clasificadores: las Redes Neuronales Artificiales de clasificación (ANNC-Artificial Neural Networks Classification), máquina de vector de soporte de tipo Smooth (SSVM-Smooth Support Vector Machine), (BAGG-Bagging), regresión logística (L2_RLR-L_2-Regularized Logistic Regression) y K vecinos más cercanos (KNN-K Nearest Neighbors). Se realizó sus respectivos entrenamientos y pruebas individuales eligiendo el clasificador KNN por su mejor calidad de detección. La RTF fue convertida en Imagen de RTF (IRTF) e IRTF de dimensionalidad reducida mediante técnicas de reducción del tamaño de imágenes (Media de la Intensidad de los Píxeles (MIP), KNN, Bilineal, Bicúbica) y de la técnica de Selección de Características (SC) de tipo (SFS-Sequential Forward Selection). Se realizaron combinaciones de los algoritmos de clasificación al aplicarse sobre un mismo conjunto de datos (IRTF, IRTF reducida, SC), con el fin de comparar el comportamiento entre ellos y con los resultados logrados de los clasificadores individuales. Estas estrategias y metodologías utilizadas fueron comparadas con diferentes métodos citados en la bibliografía y así comprobar en qué medida los resultados obtenidos apoyan nuestra hipótesis. Resultados: usando parámetros de evaluación del rendimiento (sensibilidad, especificidad y exactitud) como metodología para validar los resultados que obtuvo cada una de las estrategias empleadas, se encontró que el clasificador KNN alcanza el mejor rendimiento para RTF. Se logró para ‘FV’ una sensibilidad del 94,97% y una especificidad global del 99,27%, exactitud global del 98,47 %, y para ‘TV’ una sensibilidad del 93,47%, especificidad global del 99,39%, exactitud global del 98,97% y un tiempo de ejecución 0,1763s. Los principales resultados de la clasificación para la detección de FV obtenidos usando la técnica IRTF reducida (MIP) como entrada al clasificador KNN, mostraron un 88,27% de sensibilidad, 98,22% de especificidad global y 96,35% de exactitud global. En el caso de ‘TV’ 88,31% de sensibilidad, 98,80% de especificidad global, 98,05% de exactitud global y un tiempo de ejecución 0,024s. Al realizar combinaciones de los clasificadores, se obtuvo que la combinación ANNC_KNN_ANNC al utilizar el Método de Jerárquica (MJ) dieron una sensibilidad del 92,14%, especificidad del 98,07% y 97,07% de exactitud global para ‘FV’, una sensibilidad del 89,03%, una especificidad del 80,78%, 98,08% de exactitud global para ‘TV’ y un tiempo de ejecución entre [0,0239s; 0,0241s]. Conclusiones: la clasificación realizada en la discriminación de FV y TV demostró cómo se comportan los distintos algoritmos de clasificación tanto individuales como combinados al aplicarse sobre un mismo conjunto de características. Los resultados obtenidos mediante el algoritmos de combinación ANNC_KNN_ANNC usando los datos de IRTF reducidas fueron mejores en la detección comparado con los obtenidos mediante los algoritmos utilizados individualmente y otros multiclasificadores aplicando sobre un mismo conjunto de datos de IRTF reducidas. Además al comparar los resultados de la combinación con los obtenidos mediante KNN empleando la RTF y IRTF no reducidas son ligeramente inferiores en combinación, pero en cambio se obtiene un tiempo de ejecución menor, por lo que es de mejor utilidad para sistemas de detección en tiempo real. Después de un largo análisis es posible concluir que la metodología propuesta brinda información útil para la detección de FV con un bajo tiempo de cómputo, la cual la puede separar satisfactoriamente del resto de patologías cardiacas a la hora de diagnosticar, mejorando significativamente las posibilidades del paciente de ser manejado eficazmente al presentar un episodio con alguna de estas arritmias, lo que convierte a este trabajo en una fuente de aporte clínico en la ayuda para el diagnóstico de arritmias.Sudden death is a natural, unexpected and rapid death that occurs within a time frame of 24 Hours after the beginning of the pathological process. The most common causes of sudden death are Cardiovascular Diseases (CVD) that happen to be among the leading causes of death worldwide. In 2012, the World Health Organization (WHO) recorded 17.5 million CVD deaths, accounting for 31% of all deaths recorded in the world. One of the cardiovascular diseases with the highest mortality is Ventricular Fibrillation (VF), which is a cardiac arrhythmia caused by a disorganized electrical activity in the heart. During VF, the ventricles contract asynchronously causing an absence of an effective beat as well as a failure of blood pumping. This could result in a sudden death of the patient, if not treated properly within a few minutes. Defibrillation is the only definitive treatment of VF. It consists of the application of a high-energy electric shock to the patient's chest, thus restarting the normal cardiac electrical activity. The success of defibrillation is Inversely proportional to the interval of time it takes from the beginning of the episode to the application of the electrical charge. There are many difficulties in diagnosing VF: on the one hand there are some Intrinsic characteristics of the VF (lack of organization, irregularity, etc.), and on the other, there is a great similarity between VF and other cardiac pathologies including Ventricular Tachycardia (VT). The differentiation between VT and VF is quite complex. A wrongful diagnosis of VT instead of VF can cause serious injuries when applying the therapy to the patient. VT treatment consist of giving a high voltage electric shock to the patient, which could cause a VF. Conversely, if the VT is incorrectly interpreted as a VF, the result might be also dangerous for the patient´s life. Therefore, an effective detection method to distinguish VF from VT is very important in clinical research. In order to diagnose and treat cardiovascular diseases (for example VF), two major groups of diagnostic methods are established in cardiology: Invasive and noninvasive diagnoses. Invasive techniques require the introduction of catheters into the body, to measure the pressures in the cardiac chambers in order to record the electrical activity. Non-invasive techniques are focused on characterizing the pathophysiological state of the heart through electrodes placed directly on the patient's skin. The electrocardiogram (ECG) is a non invasive, low cost, examination which has been used as the basic diagnostic method for cardiac conduction disorders, by studying the frequency and the morphology of different waves that constitute the cardiac cycle. The ECG analysis is a good source of information to detect different types of heart disease. Due to the fact that the ECG signal is a non-stationary random signal, the time domain analysis does not prove to be sufficiently sensitive to the distortions of the waveforms on the ECG. However, these methods not always show all the information that can be extracted from ECG signals. Thereby, the frequency information, which shows additional data from the signal, is lost. Diagnosis within the frequency domain uses methods such as the fourier transform.Therefore, an analysis in the frequency domain allows to determine the frequencies within the ECG signal. On the other hand, using a frequency domain method losses the time information from the signal, resulting in a very limited method which is not useful for the analysis of non-stationary signals. Several studies have used mathematical models that combine time and spectral information in the same representation. This is Time-Frequency Representation (TFR) technique, very important in the treatment of non-stationary signals such as the ECG signal, as it distributes signal energy in a two-dimensional time-frequency space. In addition, multiple factors affect the acquisition and recording of the ECG signal: on the one hand there is the influence of the environment (50-60 Hz interference from the electrical grid, baseline, etc.), and on the other hand, physiological disturbances such as an electromyography (EMG). In the last decades, ECG noise reduction has been one of the main fields of research, since an adequate reduction of noise allows a good pre-processing of the signal, extracting from it the maximum amount of relevant information while eliminating the irrelevant one. The use of time-frequency representation images (t-f) as the direct input to the classifier is the novelty of this doctoral thesis. It is hypothesized that this method makes it easier to improve the classification results by eliminating the extraction of typical characteristics, and their corresponding loss of information. They are also used for evaluation and comparison with other authors. Materials and Methods: MIT-BIH´s Malignant Ventricular Arrhytmia and AHA (2000 series) standard data bases have been used to obtain the records of ECG signals, creating from them one set for training and one for testing the classification algorithms used. A total of 24 monitoring records (22 MIT-BIH records plus two additional from the AHA data) have been used, with a sampling frequency of 125 Hz. As part of the method a filter algorithm has been implemented in order to reduce the base line. A report that indicates the beginning of the time Window (tW) of the ECG signal is developed. First, the Hilbert Transform (HT) was applied to each window obtained. Then the RTF was applied as input to the five classifiers: Artificial Neural Networks Classification (ANNC), Smooth-type support vector machine (SSVM), bagging (BAGG), \u1d43f2Regularized Logistic Regression (L2_RLR) and K Nearest Neighbors (KNN).Their respective training and individual tests were made by choosing the KNN classifier for best quality detection. The RTF was converted into an IRTF and a reduced dimensionality IRTF by means of image size reduction techniques (Average Intensity of the Pixels (AIP), KNN, Bilineal, Bicubic) and the techniques of Selection of Characteristics (SC), specifically the Sequential Forward Selection technique (SFS). Combinations of the classification algorithms were performed when applied over the same set of data (TFRI, reduced TFRI, SC), in order to compare their behavior with the results obtained from the individual classifiers. These strategies and methodologies were compared with different methods cited in the literature to verify to what extent the results obtained support our hypothesis. Results: using performance evaluation parameters (sensitivity, specificity and accuracy) as a methodology to validate the results obtained by each one of the strategies employed, it was found that the KNN classifier achieves the best performance for TFR. For 'VF' it was obtained a sensitivity of 94.97% an overall specificity of 99.21%, and an overall accuracy of 98.47%. Whereas for 'VT, it was obtained a Sensitivity of 93.47%, an overall specificity of 99.39%, and an overall accuracy of 98.97% within a time of execution 0,1763s. The main results of the classification for the detection of VF obtained using the reduced TFRI technique (AIP) as input to the KNN classifier, showed an 88.27% of sensitivity, a 98.22% of overall specificity and a 96.35% of overall accuracy. For ´VT', it was obtained an 88.31% sensitivity, a 98.80% for global specificity, and a 98.05% of global accuracy within a runtime of 0.024s. When the classifiers were combined, it resulted that the ANNC_KNN_ANNC combination when using the Hierarchical Method (MJ) gave a sensitivity of 92.6%, a specificity of 98.68% and an overall accuracy of 97.52% for 'VF'. Whereas a Sensitivity of 89.3%, a specificity of 99.01%, and a 98.32% of overall accuracy for 'VT 'were obtained with a runtime between [0,0487s; 0,0492s]. Conclusions: the classification of VF and VT discrimination showed how the different classification algorithms (both individual and combined) behave over the same set of characteristics. The results obtained by the ANNC_KNN_ANNC combination of algorithms using the reduced TFRI data were better at detection when compared to those obtained by the algorithms used individually as well as to other multi classifiers applied over a single set of reduced TFRI data. In addition, when comparing the results of the combination with those obtained by the KNN using the non-reduced TFR and the TFRI, the latter are slightly lower. On the other hand, a shorter execution time is obtained, so the method is more useful for Real-time detection systems. After a long analysis it is possible to conclude that the proposed methodology provides useful information for the detection of VF with a low computation time. Furthermore it can separate satisfactorily the VF from the rest of cardiac pathologies at the time of diagnosis, significantly improving the patient's ability to be effectively treated when presenting an episode with one of these arrhythmias. Thus making this work a source of clinical help in the diagnosis of arrhythmias

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

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

Symbolic Dynamics Analysis: a new methodology for foetal heart rate variability analysis

Cardiotocography (CTG) is a widespread foetal diagnostic methods. However, it lacks of objectivity and reproducibility since its dependence on observer's expertise. To overcome these limitations, more objective methods for CTG interpretation have been proposed. In particular, many developed techniques aim to assess the foetal heart rate variability (FHRV). Among them, some methodologies from nonlinear systems theory have been applied to the study of FHRV. All the techniques have proved to be helpful in specific cases. Nevertheless, none of them is more reliable than the others. Therefore, an in-depth study is necessary. The aim of this work is to deepen the FHRV analysis through the Symbolic Dynamics Analysis (SDA), a nonlinear technique already successfully employed for HRV analysis. Thanks to its simplicity of interpretation, it could be a useful tool for clinicians. We performed a literature study involving about 200 references on HRV and FHRV analysis; approximately 100 works were focused on non-linear techniques. Then, in order to compare linear and non-linear methods, we carried out a multiparametric study. 580 antepartum recordings of healthy fetuses were examined. Signals were processed using an updated software for CTG analysis and a new developed software for generating simulated CTG traces. Finally, statistical tests and regression analyses were carried out for estimating relationships among extracted indexes and other clinical information. Results confirm that none of the employed techniques is more reliable than the others. Moreover, in agreement with the literature, each analysis should take into account two relevant parameters, the foetal status and the week of gestation. Regarding the SDA, results show its promising capabilities in FHRV analysis. It allows recognizing foetal status, gestation week and global variability of FHR signals, even better than other methods. Nevertheless, further studies, which should involve even pathological cases, are necessary to establish its reliability.La Cardiotocografia (CTG) è una diffusa tecnica di diagnostica fetale. Nonostante ciò, la sua interpretazione soffre di forte variabilità intra- e inter- osservatore. Per superare tali limiti, sono stati proposti più oggettivi metodi di analisi. Particolare attenzione è stata rivolta alla variabilità della frequenza cardiaca fetale (FHRV). Nel presente lavoro abbiamo suddiviso le tecniche di analisi della FHRV in tradizionali, o lineari, e meno convenzionali, o non-lineari. Tutte si sono rivelate efficaci in casi specifici ma nessuna si è dimostrata più utile delle altre. Pertanto, abbiamo ritenuto necessario effettuare un’indagine più dettagliata. In particolare, scopo della tesi è stato approfondire una specifica metodologia non-lineare, la Symbolic Dynamics Analysis (SDA), data la sua notevole semplicità di interpretazione che la renderebbe un potenziale strumento di ausilio all’attività clinica. Sono stati esaminati all’incirca 200 riferimenti bibliografici sull’analisi di HRV e FHRV; di questi, circa 100 articoli specificamente incentrati sulle tecniche non-lineari. E’ stata condotta un’analisi multiparametrica su 580 tracciati CTG di feti sani per confrontare le metodologie adottate. Sono stati realizzati due software, uno per l’analisi dei segnali CTG reali e l’altro per la generazione di tracciati CTG simulati. Infine, sono state effettuate analisi statistiche e di regressione per esaminare le correlazioni tra indici calcolati e parametri di interesse clinico. I risultati dimostrano che nessuno degli indici calcolati risulta più vantaggioso rispetto agli altri. Inoltre, in accordo con la letteratura, lo stato del feto e le settimane di gestazione sono parametri di riferimento da tenere sempre in considerazione per ogni analisi effettuata. Riguardo la SDA, essa risulta utile all’analisi della FHRV, permettendo di distinguere – meglio o al pari di altre tecniche – lo stato del feto, la settimana di gestazione e la variabilità complessiva del segnale. Tuttavia, sono necessari ulteriori studi, che includano anche casi di feti patologici, per confermare queste evidenze

Hilbert-Huang Transform: biosignal analysis and practical implementation

Any system, however trivial, is subjected to data analysis on the signals it produces. Over the last 50 years the influx of new techniques and expansions of older ones have allowed a number of new applications, in a variety of fields, to be analysed and to some degree understood. One of the industries that is benefiting from this growth is the medical field and has been further progressed with the growth of interdisciplinary collaboration. From a signal processing perspective, the challenge comes from the complex and sometimes chaotic nature of the signals that we measure from the body, such as those from the brain and to some degree the heart. In this work we will make a contribution to dealing with such systems, in the form of a recent time-frequency data analysis method, the Hilbert-Huang Transform (HHT), and extensions to it. This thesis presents an analysis of the state of the art in seizure and heart arrhythmia detection and prediction methods. We then present a novel real-time implementation of the algorithm both in software and hardware and the motivations for doing so. First, we present our software implementation, encompassing realtime capabilities and identifying elements that need to be considered for practical use. We then translated this software into hardware to aid real-time implementation and integration. With these implementations in place we apply the HHT method to the topic of epilepsy (seizures) and additionally make contributions to heart arrhythmias and neonate brain dynamics. We use the HHT and some additional algorithms to quantify features associated with each application for detection and prediction. We also quantify significance of activity in such a way as to merge prediction and detection into one framework. Finally, we assess the real-time capabilities of our methods for practical use as a biosignal analysis tool

Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores

Evaluación no invasiva del impulso neural respiratorio y su relación con la respuesta mecánica mediante el análisis de señales electromiográficas de músculos respiratorios

Respiratory muscle contraction occurs in response to the electrical stimulation of the muscles. These electrical stimuli originate in the respiratory neurons of the brainstem, are transmitted via motor nerves to the neuromuscular junctions and propagate along muscle fibers. Respiratory electromyography measures the electrical activity of respiratory muscles in response to this nerve stimulation. The neural respiratory drive (NRD) is best expressed in a phrenic neurogram, but this is not feasible in humans. Alternatively, measurements of the diaphragm electromyographic signal (EMGdi) would most likely reflect phrenic neurogram activity. EMGdi signal can be recorded using invasive methods, involving the use of needle electrodes or electrodes positioned in the esophagus at the level of the diaphragm. As a non-invasive alternative, the study of respiratory muscle activity can be addressed by surface electromyography. The onset and offset of the neural inspiratory time (nton and ntoff, respectively) are fundamentally important measurements in studies of patient-ventilator interaction, where the level of assistance delivered by the ventilator is controlled by patient demand. Cardiac artifacts (ECG) often make it difficult to utilize EMGdi. To overcome the shortcoming of the ECG, in this thesis is proposed to use sample entropy with fixed tolerance values (fSampEn), a robust technique against impulsive noise. To evaluate nton and ntoff estimation it has been carried out an experimental study with surface EMGdi signals recorded in healthy subjects during two respiratory protocols designed to evaluate the influence of different breathing patterns on the EMGdi. These protocols consisted of a stepwise increase in respiratory rate (RR) with constant fractional inspiratory time (Ti/Ttot) and a stepwise decrement in the Ti/Ttot with constant RR, respectively. The developed algorithms allowed to determine the nton and ntoff and derive the RR, Ti and Ti/Ttot neural ventilatory parameters. The EMGdi amplitude provides a real-time indirect measure of the NRD, which reflects the load on the respiratory muscles. The NRD, assessed by normalized EMGdi signals, is higher in patients with respiratory disease than in healthy subjects. To evaluate the behavior of the fSamp En, as a method for improving the measurement of NRD from EMGdi signals in the presence of cardiac activity, compared to the average rectified value and root mean square value approaches, first, these methods have been applied to synthetic EMGdi signals . Secondly, we tested the proposed methods in an experimental study with EMGdi signals recorded in healthy subjects during an incremental inspiratory load test. The EMGdi amplitude allowed to evaluate changes in the respiratory muscle activation patterns and estimate the NRD. Also, this thesis contributes to the study of the respiratory activity by the non-invasive recording of mechanomyographic low frequency (BF) activity in healthy subjects and in patients with chronic obstructive pulmonary disease, allowing the study of bilateral asynchrony of the diaphragm and the RR. Finally, we have proposed the use of concentric ring electrodes as an alternative to improve the spatial resolution of electromyographic recordings, and eliminate the problems associated with the location and orientation of the bipolar configuration. The approaches presented in this doctoral thesis based on the analysis of electromyographic and mechanomyographic signals of respiratory museles allow to extract complementary information to current use techniques of and contribute to the study of respiratory function in the clinical setting .La contracción de los músculos respiratorios se produce en respuesta a la estimulación eléctrica. Estos estímulos se originan en las neuronas respiratorias del tronco del encéfalo, se transmiten a través de los nervios motores a las uniones neuromusculares y se propagan a lo largo de las fibras musculares. La electromiografía respiratoria mide la actividad eléctrica de los músculos respiratorios en respuesta a esta estimulación nerviosa. El impulso neural respiratorio (NRD) se expresa mejor a través del neurograma frénico, pero esto no es factible en los seres humanos. Como alternativa, la medida de la señal electromiográfica del diafragma (EMGdi) refleja de forma indirecta la actividad frénica. La señal EMGdi puede registrarse utilizando métodos invasivos, lo que implica el uso de electrodos de aguja o electrodos colocados en el esófago a nivel del diafragma . Como alternativa no invasiva, el estudio de la actividad muscular respiratoria puede abordarse mediante la electromiografía de superficie. El inicio y fin del tiempo neural inspiratorio (nton y ntoff, respectivamente) son medidas de importancia en los estudios de interacción paciente-ventilador, donde el nivel de la asistencia proporcionada por el ventilador es controlado por la demanda del paciente. Los artefactos cardíacos (ECG) a menudo hacen que sea difícil de utilizar la señal EMGdi. Para superar el inconveniente de la interferencia ECG, en la presente tesis se propone utilizar la entropía muestra! con valores de tolerancia fijos (fSampEn), una técnica que es robusta contra el ruido de tipo impulsivo. Para evaluar la estimación del nton y ntoff se ha realizado un estudio experimental con señales EMGdi superficie registrada en sujetos sanos durante dos protocolos respiratorios, diseñados para evaluar la influencia de los diferentes patrones respiratorios sobre la señal EMGdi. Estos protocolos consistieron en un aumento gradual de la frecuencia respiratoria (RR) con un tiempo inspiratorio (Ti) fracciona! constante (Ti!Ttot) y en una disminución gradual en el Ti!Ttot con una RR constante, respectivamente. Los algoritmos desarrollados han permitido determinar el nton y el ntoff y derivar los parámetros ventilatorios RR, Ti, y TifTtot neurales. La amplitud de la EMGdi proporciona una medida indirecta del NRD, que refleja la carga sobre los músculos respiratorios. El NRD, evaluado en señales EMGdi normalizadas, es mayor en pacientes con enfermedades respiratorias que en sujetos sanos. Para evaluar el comportamiento de la fSampEn, como un método para mejorar la medición del NRD a partir de señales EMGdi en presencia de ECG, en comparación con los enfoques basados en el uso del valor rectificado medio y valor cuadrático medio, primero, se han aplicado estos métodos a señales EMGdi sintéticas . En segundo lugar, hemos probado los métodos propuestos en un estudio experimental con señales EMGdi registradas en sujetos sanos durante una prueba de carga inspiratoria incremental. La amplitud de la EMGdi permitió evaluar los cambios en el patrón de activación de los músculos respiratorios y estimar el NRO. Asimismo, esta tesis doctoral contribuye al estudio de la actividad respiratoria mediante el registro no invasivo de actividad mecanomiográfica de baja frecuencia (BF) en sujetos sanos y en pacientes con enfermedad obstructiva crónica, permitiendo el estudio de la asincronía bilateral del diafragma y la RR. Finalmente, hemos propuesto el uso de electrodos de anillos concéntricos como una alternativa para mejorar la resolución espacial de los registros electromiográficos, y eliminar los problemas asociados a la localización y orientación de la configuración bipolar. Los enfoques presentados en esta tesis doctoral basados en el análisis de señales electromiográficas y mecanomiográficas de los músculo