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        <article-title>Electrocardiogram (ECG) Analysis From Domain Experience to Classication</article-title>
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        <contrib contrib-type="author">
          <string-name>Jun Dong</string-name>
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        <contrib contrib-type="author">
          <string-name>Chinese Academy of Sciences</string-name>
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      <p>The core issue of artificial intelligence is thinking simulation. We use
the electrocardiogram (ECG) as an example. Here, Abstract and logical
thinking work alongside with imagery thinking and experience analysis,
and the physicians diagnosis has both macro precepts together with
micro particulars. Meanwhile, a few problems restrict clinical acceptance
of the many theoretical ECG analysis methods developed during the
last forty years, such as whether they are identical with thinking
patterns in the physicians’ brain, and whether extracted ECG features are
sufficient and necessary. Based on the Chinese Cardiovascular Diseases
Database (CCDD, http://58.210.56.164: 88/ccdd/)we have designed,
physicians diagnosis experience and relevant formal representation are
discussed, implicit knowledge that is hard to express is analyzed, and
fusion of rules inference and deep learning for ECG classification is
implemented. We have got the better classification result comparing with
the state-of-the-art approaches.</p>
      <p>Copyright © by the paper’s authors. Copying permitted for private and academic purposes.</p>
      <p>In: T. A˚gotnes, B. Liao, Y.N. Wang (eds.): Proceedings of the first Chinese Conference on Logic and Argumentation (CLAR 2016),
Hangzhou, China, 2-3 April 2016, published at http://ceur-ws.org</p>
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