<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322016000100063</article-id>
<article-id pub-id-type="doi">10.17488/RMIB.37.1.5</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Determinación del Tamaño Óptimo de Modelos HMM-GMM para Clasificación de las Señales Bioacústicas]]></article-title>
<article-title xml:lang="en"><![CDATA[Determining optimal size of HMM-GMM models to classify bio-acoustic signal]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mayorga-Ortiz]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Druzgalski]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Miranda Vega]]></surname>
<given-names><![CDATA[J.E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zeljkovic]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Mexicali Depto. de Posgrado ]]></institution>
<addr-line><![CDATA[Mexicali B.C.]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,California State University Elec. Eng. Dept. ]]></institution>
<addr-line><![CDATA[Long Beach CA]]></addr-line>
<country>USA</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,The Lincoln University CPES Dept. ]]></institution>
<addr-line><![CDATA[ PA]]></addr-line>
<country>USA</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2016</year>
</pub-date>
<volume>37</volume>
<numero>1</numero>
<fpage>63</fpage>
<lpage>79</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322016000100063&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322016000100063&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322016000100063&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este artículo está relacionado con el análisis y la propuesta de una arquitectura HMM-GMM para clasificación de señales HS y LS, haciendo un énfasis en el tamaño del modelo. Actualmente, las enfermedades respiratorias y cardiovasculares son un problema a nivel mundial y con una alta mortandad, esto podría ser disminuido mediante un diagnóstico temprano y objetivo; las herramientas digitales y el empleo de reconocimiento de patrones ampliarían las perspectivas de aplicación. Particularmente, aquí se demuestra que los modelos HMM-GMM son eficientes para consultorios de atención primaria, así mismo los extractores de características tales como MFCC y Cuantiles mejoran la tarea de clasificación. Si bien la visualización con siluetas, dendrogramas y algoritmos tales como BIC no son concluyentes cuando se aplican GMM's, no obstante sí fue el punto de partida para dimensionar el tamaño del modelo, disminuyendo la cantidad de experimentos con distintos tamaños del mismo. Adicionalmente, se constata que la estructura de señales normales HS y LS cambian cuando hay patologías y permite la clasificación aplicando MFCC o Cuantiles. Además, se observa que con una gran cantidad de datos se podrían obtener modelos más robustos y adaptados, pero esto no es una limitante para el cálculo de los modelos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This paper demonstrates the analysis and proposed HMM-GMM models architecture to classify heart and lung sounds (HS and LS) signals emphasizing the model size optimization. Respiratory and cardiovascular diseases continue to represent one of the major worldwide healthcare problems associated with a liigli mortality rate, wliicli can be reduced by an early and effective diagnosis; in this context, the use of digital tools utilizing signal pattern recognition allows efficient screening for abnormalities and their quantitative assessment. In particular, the HMM-GMM models demonstrated their efficiency in normal and traditionally noisy environments in light of very low intensities of these auscultation signals used as diagnostic indicators. Furthermore, applied MFCC and Quantiles feature extractors improve overall classification. While characterization with silhouettes, dendrograms and algorithms such as BIC was inconclusive when GMM was applied, however they were useful as a starting point in the determination of a size of the model as it allowed a reduction in the number of iterations considering different model size. In addition one can note that application of MFCC or Quantiles allowed differentiating the characteristics of normal HS and LS from those associated with pathological conditions. Furthermore, it was observed that a large amount of data leads to more robust and adapted models, but does not limit the calculation demand. Overall, this approach may enhance efficiency and precision of the diagnostic screening for abnormal auscultation indicators.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[cuantíl]]></kwd>
<kwd lng="es"><![CDATA[Coeficientes Cepstrales en Frecuencia Mel (MFCC)]]></kwd>
<kwd lng="es"><![CDATA[Modelos Ocultos de Markov (HMM)]]></kwd>
<kwd lng="es"><![CDATA[Modelos Mezclados Gaussianos (GMM)]]></kwd>
<kwd lng="en"><![CDATA[quantil]]></kwd>
<kwd lng="en"><![CDATA[Mel Frequency Cepstrum Coefficients (MFCC)]]></kwd>
<kwd lng="en"><![CDATA[Hidden Markov Models (HMM)]]></kwd>
<kwd lng="en"><![CDATA[Gaussian Mixture Models (GMM)]]></kwd>
</kwd-group>
</article-meta>
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