<?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>1684-1859</journal-id>
<journal-title><![CDATA[Revista Cubana de Informática Médica]]></journal-title>
<abbrev-journal-title><![CDATA[RCIM]]></abbrev-journal-title>
<issn>1684-1859</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de La Habana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1684-18592023000100012</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A Nonlinear Identification Approach for Gait Analysis of Amyotrophic Lateral SclerosisPatients]]></article-title>
<article-title xml:lang="es"><![CDATA[Enfoque de identificación no lineal para análisis de la marcha en pacientes con esclerosis lateralamiotrófica]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aznielle]]></surname>
<given-names><![CDATA[Tania Yadira]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernandez Caceres]]></surname>
<given-names><![CDATA[Jose Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centro de Neurociencias de Cuba  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<volume>15</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1684-18592023000100012&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1684-18592023000100012&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1684-18592023000100012&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Gait data analysis, is giving mixing results regarding locomotion changes associated to Amyotrophic Lateral Sclerosis (ALS) development; the need has been claimed for new tools. We applied a nonlinear identification approach to the study of gait data from both healthy and ALS patients, available from Physionet.org. Kernel nonparametric nonlinear autoregression allowed to obtain noise-free realizations (NFR) that mimicked original traces, though correlation between original data and corresponding NFR was lower among ALS patients (p=0.03), suggesting a higher contribution of stochastic influences. Visual inspection of phase portraits, reconstructed from NFR via Takens theorem application, suggested dynamics differences between control subjects and patients. This was confirmed when phase portrait features were quantified and submitted to discriminant analysis (89% of correct classifications; 24/28). Application of a nonlinear dissimilarity measure for comparing pairs gait recordings, defined as a distance between underlying nonlinear autoregressive functions allowed an excellent separation between ALS and controls, via multidimensional scaling. Obtained projection map clearly suggested that ALS traces lay in a narrower dynamical space. This might reflect the known fact about neuronal degeneration accompanying ALS progression. When dissimilarity matrix principal components were introduced as predicting variables, discriminant analysis yielded an 82% of correct classifications (23/28). Overall, our results suggest that a nonlinear identification approach, centered in the characterization of the dynamics of the gait process can bring new insights to gait data interpretation.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El análisis de datos de la marcha, está dando resultados mixtos con respecto a los cambios de locomoción asociados con el desarrollo de la esclerosis lateral amiotrófica (ELA). Se ha reivindicado la necesidad de nuevas herramientas de análisis de datos de la marcha. Aplicamos un enfoque de identificación no lineal al estudio de los datos de la marcha de pacientes sanos y con ELA, disponibles en Physionet.org. La auto-regresión no lineal no paramétrica del núcleo, permitió obtener realizaciones libres de ruido (NFR) que imitaban las trazas originales, aunque la correlación entre los datos originales y la NFR correspondiente fue menor entre los pacientes con ELA (p = 0,03), lo que sugiere una mayor contribución de las influencias estocásticas. La inspección visual de los retratos de fase, reconstruidos a partir de NFR mediante la aplicación del teorema de Takens, sugirió diferencias dinámicas entre los sujetos de control y los pacientes. Esto se confirmó cuando se cuantificaron las características del retrato de fase y se sometieron a un análisis discriminante (89 % de clasificaciones correctas; 24/28). La aplicación de una medida de disimilitud no lineal para comparar registros de marcha de pares, definida como una distancia entre funciones auto-regresivas no lineales subyacentes, permitió una excelente separación entre ALS y controles, a través de una escala multidimensional. El mapa de proyección obtenido sugirió claramente que las huellas de ALS se encuentran en un espacio dinámico más estrecho. Esto podría reflejar el hecho conocido sobre la degeneración neuronal que acompaña a la progresión de la ELA. Cuando se introdujeron los componentes principales de la matriz de disimilitud como variables predictoras, el análisis discriminante arrojó un 82% de clasificaciones correctas (23/28). En general, nuestros resultados sugieren que un enfoque de identificación no lineal, centrado en la caracterización de la dinámica del proceso de la marcha, puede aportar nuevos conocimientos a la interpretación de los datos de la marcha.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[gait data analysis]]></kwd>
<kwd lng="en"><![CDATA[non linear approach]]></kwd>
<kwd lng="en"><![CDATA[Takens&#8217; theorem]]></kwd>
<kwd lng="es"><![CDATA[análisis de la marcha]]></kwd>
<kwd lng="es"><![CDATA[enfoque no lineal]]></kwd>
<kwd lng="es"><![CDATA[teorema de Takens]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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