Preclustering of Electrocardiographic Signals Using Left-to-Right Hidden Markov Models

Micó, Pau; Cuesta, David; Novák, Daniel

doi:10.1007/978-3-540-27868-9_103

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…The one-way Markov chain is a very useful model in a variety of application areas of information system models. A few examples are hidden Markov modeling of speech signals (see, e.g., [25] and references therein), the segmentation of signals, such as those that govern the evolution of the fading process of a communication channel (or channels that "heat up" [26]), the segmentation of electrocardiographic signals (see, e.g., [27]), beat tracking in audio signals (see, e.g., [29]), and even handwritten text recognition [27].…”

Section: Applicationsmentioning

confidence: 99%

Bose–Einstein condensation in large deviations with applications to information systems

Merhav¹,

Kafri²

2010

J. Stat. Mech.

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Abstract. We study the large deviations behavior of systems that admit a certain form of a product distribution, which is frequently encountered both in Physics and in various information system models. First, to fix ideas, we demonstrate a simple calculation of the large deviations rate function for a single constraint (event). Under certain conditions, the behavior of this function is shown to exhibit an analogue of Bose-Einstein condensation (BEC). More interestingly, we also study the large deviations rate function associated with two constraints (and the extension to any number of constraints is conceptually straightforward). The phase diagram of this rate function is shown to exhibit as many as seven phases, and it suggests a two-dimensional generalization of the notion of BEC (or more generally, a multidimensional BEC). While the results are illustrated for a simple model, the underlying principles are actually rather general. We also discuss several applications and implications pertaining to information system models.

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Section: Applicationsmentioning

confidence: 99%

Bose–Einstein condensation in large deviations with applications to information systems

Merhav¹,

Kafri²

2010

J. Stat. Mech.

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“…If the selected features does not represent the intrinsic quality of each object, the final results derived from clustering process will not become acceptable. The object feature selection can be based on many different techniques [7], [8], [9]. In this case and because of its high-speed and mathematical simplicity, the PCA has been chosen [10].…”

Section: Clustering Taskmentioning

confidence: 99%

Clustering Improvement for Electrocardiographic Signals

Micó¹,

Cuesta²,

Novák

2005

Image Analysis and Processing – ICIAP 2005

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Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECG. These signals normally include several channels and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole Holter ECG to find all those beats whose morphology differ from the normal cardiac rhythm. The later analysis of these abnormal beats yields a diagnostic from the pacient's heart condition. In this paper we compare the performance among several clustering methods applied over the beats processed by Principal Component Analysis (PCA). Moreover, an outlier removing stage is added, and a cluster estimation method is included. Quality measurements, based on ECG labels from MIT-BIH database, are developed too. At the end, some results-accuracy values among several clustering algorithms is presented.

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“…Izquierda a derecha: método de inicialización que se basa en la morfología del latido seleccionado como centroide para obtener una estimación ajustada de los parámetros del modelo [MCN04b].…”

Section: Selección Y Modelado Del Latido Inicialunclassified

“…Una vez elegidos los dos latidos que constituyen los centroides iniciales y utilizando alguno de los métodos de estimación anteriores, se calculará un HMM para cada uno de ellos [MCN04b], lo que nos permitirá obtener en el siguiente paso la matriz de similitud por proyección de los objetos sobre el modelo de cada centroide.…”

Section: Selección Y Modelado De Los Centroides Inicialesunclassified

Nuevos desarrollos y aplicaciones basados en métodos estocásticos para el agrupamiento no supervisado de latidos en señales electrocardiográficas

Tormos¹

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The work developed and presented in this thesis continues the research line started with another PhD. thesis entitled "Estudio de métodos para procesamiento y agrupación de señales electrocardiográficas". The algorithms and methods used here have been developed to automatically process the information contained within a special kind of long-time electrocardiographic registers (called Holter ECG). The aim of the work is to provide doctors and cardiologists with a complete set of tools that make them easier the task of analysing and diagnosing the cardiac diseases. To perform this, we will apply to the Holter ECG a clustering process in order to automatically group the heart beats that compose the signal into a very few clusters from where doctors, by means of the manual inspection of a representative beat from each cluster, easily and quickly provide a diagnosis. Finally, to achieve the clustering objective, we will study the ECG morphological features using the large amount of databases available through the internet, the development of new ECG signal applications, the comparison among methods, and some algorithm optimization tasks have been also performed. A polygonal approximation algorithm used for ECG compression, the Principal Component Analysis (PCA) scheme applied to the feature selection stage or heart beat modelling by means of the Hidden Markov Models (HMM) for feature reduction too. The application of all the methods described above has become an improvement of the final Holter ECG clustering process. IV Abstract ResumenEl trabajo que a continuación se presenta, pretende dar continuidad a la línea de trabajo iniciada con una tesis previa titulada "Estudio de méto-dos para procesamiento y agrupación de señales electrocardiográficas". La problemática a abordar es la misma que en la tesis mencionada: reducción del número de latidos a examinar el un registro electrocardiográfico (ECG) de larga duración (conocido como Holter), mediante técnicas de agrupamiento no supervisado. Sin embargo, en este caso y como técni-ca de extracción de características se utiliza la aproximación poligonal (Polygonal Approximation, PA) y además, en la etapa de reducción de características, se usará un método estocástico basado en los modelos ocultos de Markov (Hidden Markov Models, HMM) y uno estadístico basado en el análisis de componentes principales (Principal Component Analysis, PCA). En esta tesis se describen las aportaciones realizadas en estas tres áreas: PA, HMM y PCA, aplicadas a esta problemática concreta. La validez de los métodos viene refrendada por un conjunto amplio de experimentos utilizando como fuentes de datos las señales ECG disponibles en la base de datos del Massachusetts Institute of Technology (MIT). Como conclusión se enumeran las mejoras obtenidas de la aplicación de estos métodos sobre el proceso de clustering de latidos sobre señales ECG de tipo Holter. VI Resumen ResumEl treball que a continuació es presenta vol donar continuïtat a la lí-nia de treball iniciada amb una tesi prèvia titulada...

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Preclustering of Electrocardiographic Signals Using Left-to-Right Hidden Markov Models

Cited by 5 publications

References 7 publications

Bose–Einstein condensation in large deviations with applications to information systems

Bose–Einstein condensation in large deviations with applications to information systems

Clustering Improvement for Electrocardiographic Signals

Nuevos desarrollos y aplicaciones basados en métodos estocásticos para el agrupamiento no supervisado de latidos en señales electrocardiográficas

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