<?xml version="1.0" encoding="utf-8"?>
<!-- generator="Joomla! 1.5 - Open Source Content Management" -->
<feed xmlns="http://www.w3.org/2005/Atom"  xml:lang="en-gb">
	<title type="text">Epilepsy Ontology</title>
	<subtitle type="text">Active Constraint Technology for Ill-defined or Volatile Environment</subtitle>
	<link rel="alternate" type="text/html" href="http://www.active-fp7.eu"/>
	<id>http://www.active-fp7.eu/index.php/download/epilont</id>
	<updated>2015-04-09T07:18:57Z</updated>
	<generator uri="http://joomla.org" version="1.5">Joomla! 1.5 - Open Source Content Management</generator>
<link rel="self" type="application/atom+xml" href="http://www.active-fp7.eu/index.php/download/epilont?format=feed&amp;type=atom" />
	<entry>
		<title>Epilepsy Ontology</title>
		<link rel="alternate" type="text/html" href="http://www.active-fp7.eu/index.php/download/epilont/146-epilepsy"/>
		<published>2014-02-21T09:54:44Z</published>
		<updated>2014-02-21T09:54:44Z</updated>
		<id>http://www.active-fp7.eu/index.php/download/epilont/146-epilepsy</id>
		<author>
			<name>Administrator</name>
		<email>vaccarella.alberto@gmail.com</email>
		</author>
		<summary type="html">&lt;p&gt;The Epilepsy ontology is an ontology that describes the process of diagnosis about epilepsy. Through this ontology it is possible to define and describe every epileptic seizure in a semiological way. We defined, in collaboration with neurophysiologists, a set of 30 symptoms that could appear during a seizure.&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;The graph that came out from this ontology is used to automatically classify temporal or extratemporal lobe epilepsies, starting from observed symptoms in a seizure.&lt;/span&gt;&lt;/p&gt;
&lt;p&gt; &lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;You can download the ontology here: &lt;a href=&quot;http://www.active-fp7.eu/index.php/documentation/doc_download/860-epilepsyowl&quot;&gt;Epilepsy.owl&lt;/a&gt;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;Please reference the following publication:&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;Kassahun Y, &lt;/span&gt;Perrone R&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;, &lt;/span&gt;De Momi E&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;, Berghofer E, Tassi L, Canevini MP, Spreafico R, &lt;/span&gt;Ferrigno G&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;, Kirchner F. 2014. Automatic Classification of Epilepsy Types Using Ontology-based and Genetic based Machine Learning. &lt;em&gt;Artificial Intelligence in Medicine&lt;/em&gt;.&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;URL: &lt;a href=&quot;http://www.sciencedirect.com/science/article/pii/S0933365714000207&quot; target=&quot;_blank&quot;&gt;http://www.sciencedirect.com/science/article/pii/S0933365714000207&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;Affiliations:&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;- Fachbereich 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 5, D-28359 Bremen, Germany;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;- Epilepsy Center (DDEP), San Paolo Hospital, Via A. Di Rudini 8, 20142 Milan, Italy;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;img src=&quot;http://upload.wikimedia.org/wikipedia/it/b/be/Logo_Politecnico_Milano.png&quot; border=&quot;0&quot; width=&quot;50&quot; height=&quot;50&quot; style=&quot;line-height: 1.3em;&quot; /&gt; &lt;img src=&quot;http://www.dfki.de/web/logo.jpg&quot; border=&quot;0&quot; title=&quot;dfki&quot; width=&quot;214&quot; height=&quot;40&quot; style=&quot;line-height: 1.3em;&quot; /&gt;&lt;img src=&quot;http://www.active-fp7.eu/images/stories/ddep.png&quot; border=&quot;0&quot; width=&quot;95&quot; height=&quot;63&quot; style=&quot;line-height: 1.3em;&quot; /&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt; &lt;/span&gt;&lt;/p&gt;</summary>
		<content type="html">&lt;p&gt;The Epilepsy ontology is an ontology that describes the process of diagnosis about epilepsy. Through this ontology it is possible to define and describe every epileptic seizure in a semiological way. We defined, in collaboration with neurophysiologists, a set of 30 symptoms that could appear during a seizure.&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;The graph that came out from this ontology is used to automatically classify temporal or extratemporal lobe epilepsies, starting from observed symptoms in a seizure.&lt;/span&gt;&lt;/p&gt;
&lt;p&gt; &lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;You can download the ontology here: &lt;a href=&quot;http://www.active-fp7.eu/index.php/documentation/doc_download/860-epilepsyowl&quot;&gt;Epilepsy.owl&lt;/a&gt;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;Please reference the following publication:&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;Kassahun Y, &lt;/span&gt;Perrone R&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;, &lt;/span&gt;De Momi E&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;, Berghofer E, Tassi L, Canevini MP, Spreafico R, &lt;/span&gt;Ferrigno G&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;, Kirchner F. 2014. Automatic Classification of Epilepsy Types Using Ontology-based and Genetic based Machine Learning. &lt;em&gt;Artificial Intelligence in Medicine&lt;/em&gt;.&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;URL: &lt;a href=&quot;http://www.sciencedirect.com/science/article/pii/S0933365714000207&quot; target=&quot;_blank&quot;&gt;http://www.sciencedirect.com/science/article/pii/S0933365714000207&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;font-family: Helvetica, Arial, sans-serif; line-height: 15.600000381469727px;&quot;&gt;&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;Affiliations:&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;- Fachbereich 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 5, D-28359 Bremen, Germany;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt;- Epilepsy Center (DDEP), San Paolo Hospital, Via A. Di Rudini 8, 20142 Milan, Italy;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;img src=&quot;http://upload.wikimedia.org/wikipedia/it/b/be/Logo_Politecnico_Milano.png&quot; border=&quot;0&quot; width=&quot;50&quot; height=&quot;50&quot; style=&quot;line-height: 1.3em;&quot; /&gt; &lt;img src=&quot;http://www.dfki.de/web/logo.jpg&quot; border=&quot;0&quot; title=&quot;dfki&quot; width=&quot;214&quot; height=&quot;40&quot; style=&quot;line-height: 1.3em;&quot; /&gt;&lt;img src=&quot;http://www.active-fp7.eu/images/stories/ddep.png&quot; border=&quot;0&quot; width=&quot;95&quot; height=&quot;63&quot; style=&quot;line-height: 1.3em;&quot; /&gt;&lt;/p&gt;
&lt;p&gt;&lt;span style=&quot;line-height: 1.3em;&quot;&gt; &lt;/span&gt;&lt;/p&gt;</content>
	</entry>
</feed>
