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    <user id="27187"><![CDATA[27187]]></user>
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  <created>1322577553</created>
  <changed>1475891805</changed>
  <title><![CDATA[SIAC Seminar - Sensor-based Cardiac System Informatics and Control]]></title>
  <body><![CDATA[<p>TITLE: Sensor-based Cardiac System Informatics and Control</p><p>SPEAKER; Dr. Hui Yang</p><p>ABSTRACT:</p><p>&nbsp;Medical
        devices in the 21st century
        are capable of monitoring the cardiac electrical activities in
        real time and
        lead to the proliferation of patient monitoring signals. With
        massive
        healthcare recordings readily available, there is dire need for
        the extraction
        of knowledge pertinent to the cardiac disease process, thereby
        leading to the early
        identification and control of cardiac disorders. We intertwined
        the approaches
        of physics-based modeling and sensor-based data fusion to
        promote the study of
        cardiac system informatics and control. The computational
        cardiac models not
        only overcome the practical and ethical limitations in physical
        experiments but
        also provide predictive insights on the underlying pathological
        mechanisms. Here,
        we modeled the variations of cardiac electrical signaling due to
        changes in
        glycosylation of a voltage-gated K+ channel, hERG, responsible
        for late phase 2
        and phase 3 of the human ventricular action potential (AP). The
        multi-scale cardiac
        model is developed to integrate the measured changes in hERG
        channels under different
        glycosylation treatments, and further predicts the electrical
        behaviors of
        cardiac cells and tissues (cable/ring). The experimental results
        show that
        reduced glycosylation acts to shorten the repolarization period
        of cardiac APs,
        and distort the AP propagation in cardiac tissues. On the other
        hand, we
        developed an approach of multiscale recurrence analysis to study
        the cardiac pathological
        behaviors in the space-time domain, as opposed to the
        conventional time delay
        reconstructed phase space from a single ECG trace. Few, if any,
        previous
        approaches studied the relationships between cardiac disorders
        and multiscale recurrence
        patterns underlying the cardiac vectorcardiogram (VCG) signals.
        The integration
        of wavelets and nonlinear dynamics was experimentally shown to
        facilitate the
        prominence of hidden pathological properties that are usually
        buried in a
        single scale view. Finally, future research directions in the
        area will be
        discussed.</p>]]></body>
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      <value><![CDATA[Sensor-based Cardiac System Informatics and Control]]></value>
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  <field_time>
    <item>
      <value><![CDATA[2011-12-06T10:00:00-05:00]]></value>
      <value2><![CDATA[2011-12-06T11:00:00-05:00]]></value2>
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      <timezone><![CDATA[America/New_York]]></timezone>
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  <field_audience>
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  <field_contact>
    <item>
      <value><![CDATA[<p>Roshan Vengazhiyil</p>]]></value>
    </item>
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      <value><![CDATA[]]></value>
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      <email><![CDATA[]]></email>
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  <og_groups>
          <item>1242</item>
      </og_groups>
  <og_groups_both>
          <item><![CDATA[School of Industrial and Systems Engineering (ISYE)]]></item>
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          <item>
        <tid>1795</tid>
        <value><![CDATA[Seminar/Lecture/Colloquium]]></value>
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