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PRODID:IEEE vTools.Events//EN
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TZID:Europe/Zagreb
BEGIN:DAYLIGHT
DTSTART:20240331T030000
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TZOFFSETTO:+0200
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DTSTART:20241027T020000
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BEGIN:VEVENT
DTSTAMP:20241004T084357Z
UID:A0CDA89B-0DA4-443D-A6B3-6C9703C6042F
DTSTART;TZID=Europe/Zagreb:20240925T100000
DTEND;TZID=Europe/Zagreb:20240925T110000
DESCRIPTION:In this lecture\, we will explore signal processing and artific
 ial intelligence techniques for detecting intra-beat waves in long-term el
 ectrocardiogram (ECG) signals. A particular focus will be placed on the in
 terpretability of deep learning models\, such as transformers\, which is c
 rucial in the healthcare field to ensure that clinicians can trust and rel
 y on the results.\n\nCo-sponsored by: University of Zagreb Faculty of Elec
 trical Engineering and Computing\n\nSpeaker(s): Carmen Plaza Seco\n\nRoom:
  D033\, Bldg: D\, University of Zagreb Faculty of Electrical Engineering a
 nd Computing\, Unska 3\, Zagreb\, Grad Zagreb\, Croatia\, 10000
LOCATION:Room: D033\, Bldg: D\, University of Zagreb Faculty of Electrical 
 Engineering and Computing\, Unska 3\, Zagreb\, Grad Zagreb\, Croatia\, 100
 00
ORGANIZER:tomislav.petkovic.jr@fer.hr
SEQUENCE:15
SUMMARY:Detection of Intra-beat Waves on Ambulatory ECG: Interpretable Deep
  Learning Approaches
URL;VALUE=URI:https://events.vtools.ieee.org/m/434507
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 11.0p
 t\; line-height: 107%\; font-family: &#39;Aptos&#39;\,sans-serif\; mso-ascii-theme
 -font: minor-latin\; mso-fareast-font-family: Aptos\; mso-fareast-theme-fo
 nt: minor-latin\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family
 : &#39;Times New Roman&#39;\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language:
  EN-US\; mso-fareast-language: EN-US\; mso-bidi-language: AR-SA\;&quot;&gt;In this
  lecture\, we will explore signal processing and artificial intelligence t
 echniques for detecting intra-beat waves in long-term electrocardiogram (E
 CG) signals. A particular focus will be placed on the interpretability of 
 deep learning models\, such as transformers\, which is crucial in the heal
 thcare field to ensure that clinicians can trust and rely on the results.&lt;
 /span&gt;&lt;/p&gt;
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