{"id":95178,"date":"2018-03-11T10:15:05","date_gmt":"2018-03-11T10:15:05","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/aplicacion-de-la-transformada-wavelet-al-filtrado-de-senales-electrocardiograficas\/"},"modified":"2018-03-11T10:15:05","modified_gmt":"2018-03-11T10:15:05","slug":"aplicacion-de-la-transformada-wavelet-al-filtrado-de-senales-electrocardiograficas","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/aplicacion-de-la-transformada-wavelet-al-filtrado-de-senales-electrocardiograficas\/","title":{"rendered":"Aplicaci\u00f3n de la transformada wavelet al filtrado de se\u00f1ales electrocardiogr\u00e1ficas."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Margarita Mora Carbonell <\/strong><\/h2>\n<p>Desde su aparici\u00f3n en los a\u00f1os 80, la transformada wavelet se ha utilizado en m\u00faltiples aplicaciones de la ciencia y la ingenier\u00eda, principalmente en el marco del procesamiento de se\u00f1ales tales como biom\u00e9dicas, s\u00edsmicas, audio, radar, entre otras.  en este caso particular de procesamiento de se\u00f1ales, una de las aplicaciones m\u00e1s extendidas de la transformada wavelet es la reducci\u00f3n de ruido, siendo precisamente esta aplicaci\u00f3n el marco en el que se engloba la presente tesis.  por tanto, el trabajo que a continuaci\u00f3n se presenta profundiza en el proceso de reducci\u00f3n de ruido, en el caso particular de se\u00f1ales electrocardiogr\u00e1ficas, posiblemente las m\u00e1s utilizadas en el \u00e1mbito biom\u00e9dico.  en primer lugar, se estudia y caracteriza el comportamiento de las wavelets ortogonales m\u00e1s usuales frente al ruido blanco gaussiano y ruido real en se\u00f1ales electrocardiogr\u00e1ficas, utilizando las funciones de umbralizaci\u00f3n tradicionales (hard y soft) en m\u00e9todos de reducci\u00f3n de ruido. A continuaci\u00f3n, se propone una nueva funci\u00f3n de umbralizaci\u00f3n que mejore las prestaciones de los m\u00e9todos actuales de reducci\u00f3n de ruido basados en la transformada wavelet.  en segundo lugar, para optimizar a\u00fan m\u00e1s las prestaciones de esta herramienta a nivel global en se\u00f1ales de larga duraci\u00f3n, se estudian m\u00e9todos de segmentaci\u00f3n autom\u00e1tica de se\u00f1ales basados en cambios en el nivel del ruido. En concreto, se propone un nuevo m\u00e9todo de segmentaci\u00f3n usando la correlaci\u00f3n entre cambios en el nivel de entrop\u00eda de las se\u00f1ales estimados mediante entrop\u00eda muestral, y cambios en el nivel de potencia del ruido. De esta forma, se pueden calcular los umbrales localmente en zonas de nivel de ruido estacionario, en lugar depromedios globales que conducen a una umbralizaci\u00f3n sub\u00f3ptima.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Aplicaci\u00f3n de la transformada wavelet al filtrado de se\u00f1ales electrocardiogr\u00e1ficas.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Aplicaci\u00f3n de la transformada wavelet al filtrado de se\u00f1ales electrocardiogr\u00e1ficas. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Margarita Mora Carbonell <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Polit\u00e9cnica de Valencia<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 13\/07\/2009<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Enrique Jorda Mora<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: alfredo Peris manguillot <\/li>\n<li>daniel Novak (vocal)<\/li>\n<li>pedro Jos\u00e9 Miana sanz (vocal)<\/li>\n<li>Francisco Balibrea gallego (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Margarita Mora Carbonell Desde su aparici\u00f3n en los a\u00f1os 80, la transformada wavelet se ha utilizado en [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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