A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation

Accurately differentiating between ventricular fibrillation (VF) and ventricular tachycardia (VT) episodes is crucial in preventing potentially fatal misinterpretations. If VT is misinterpreted as VF, the patient will receive an unnecessary shock that could damage the heart; conversely, if VF is incorrectly interpreted as VT, the result will be life-threatening. In this study, a new method called semantic mining is used to characterize VT and VF episodes by extracting their significant characteristics (the frequency, damping coefficient and input signal). This newly proposed method was tested using a widely recognized database provided by the Massachusetts Institute of Technology (MIT) and achieved high detection accuracy of 96.7%. The semantic mining technique was capable of completely discriminating between normal rhythms and VT and VF episodes without any false detections and also distinguished VT and VF episodes from one another with a recognition sensitivity of 94.1% and 95.2% for VT and VF, respectively

Ventricular arrhythmias classification and onset determination system

Accurately differentiating between ventricular fibrillation (VF) and ventricular tachycardia (VT) episodes is crucial in preventing potentially fatal missed interpretations that could lead to needless shock to the patients, resulting in damaging the heart. Apart from accurately classifying between VT and VF, the predetermination of the onset of the ventricular arrhythmias is also important in order to allow for more efficient monitoring of patients and can potentially save one’s life. Thus, this research intends to focus on developing a system called Classification and Onset Determination System (CODS) that is able to classify, track and monitor ventricular arrhythmias by using a method called Second Order Dynamic Binary Decomposition (SOD-BD) technique. Two significant characteristics (the natural frequency and the input parameter) were extracted from Electrocardiogram (ECG) signals that are provided by Physiobank database and analyzed to find the significant differences for each ventricular arrhythmia types and classify the ECGs accordingly (N, VT and VF). The outcome from these ECG extractions was also used to locate the onset of ventricular arrhythmia that is useful to predict the occurrence of the heart abnormalities. All the ECGs analysis, parameters extraction, classification techniques, and the CODS are developed using LabVIEW software

COMPUTER AIDED DIAGNOSIS OF VENTRICULAR ARRHYTHMIAS FROM ELECTROCARDIOGRAM LEAD II SIGNALS

In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and Naïve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100% sensitivity while best previous work got 84.6%

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

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. 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