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PRODID:IEEE vTools.Events//EN
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TZID:Europe/Zagreb
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DTSTART:20240331T030000
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DTSTAMP:20241004T084458Z
UID:F4FF1654-035E-4D55-9C40-B994DB84C032
DTSTART;TZID=Europe/Zagreb:20240926T100000
DTEND;TZID=Europe/Zagreb:20240926T230000
DESCRIPTION:In this lecture\, we will focus on the characterization and det
 ection of T-Wave Alternans (TWA)\, a micro-volt fluctuation in electrocard
 iogram (ECG) signals that has been identified as a risk factor for serious
  cardiac conditions such as malignant arrhythmias and sudden cardiac death
 . We will explore a range of signal processing methods and artificial inte
 lligence techniques\, including machine learning and deep learning\, aimed
  at detecting TWA in real-world ambulatory environments to help identify p
 atients at risk of developing these life-threatening conditions. Additiona
 lly\, we will provide insights into the explainability and interpretabilit
 y of the models used\, highlighting how these aspects contribute to buildi
 ng trust in AI-driven diagnostics and ensuring that the predictions are tr
 ansparent and understandable for clinical practitioners.\n\nCo-sponsored b
 y: University of Zagreb Faculty of Electrical Engineering and Computing\n\
 nSpeaker(s): Lidia Pascual Sánchez\, Carmen Plaza Seco\n\nRoom: D033\, Bl
 dg: D\, Faculty of Electrical Engineering and Computing\, Unska 3\, Zagreb
 \, Grad Zagreb\, Croatia\, 10000
LOCATION:Room: D033\, Bldg: D\, Faculty of Electrical Engineering and Compu
 ting\, Unska 3\, Zagreb\, Grad Zagreb\, Croatia\, 10000
ORGANIZER:luka.jelic@fer.hr
SEQUENCE:45
SUMMARY:T-Wave Alternans Detection with Machine and Deep Learning Technique
 s in Real Ambulatory Environments
URL;VALUE=URI:https://events.vtools.ieee.org/m/434509
X-ALT-DESC:Description: <br /><p><span lang="EN-US" style="font-size: 11.0p
 t\; line-height: 107%\; font-family: 'Aptos'\,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
 : 'Times New Roman'\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language:
  EN-US\; mso-fareast-language: EN-US\; mso-bidi-language: AR-SA\;">In this
  lecture\, we will focus on the characterization and detection of T-Wave A
 lternans (TWA)\, a micro-volt fluctuation in electrocardiogram (ECG) signa
 ls that has been identified as a risk factor for serious cardiac condition
 s such as malignant arrhythmias and sudden cardiac death. We will explore 
 a range of signal processing methods and artificial intelligence technique
 s\, including machine learning and deep learning\, aimed at detecting TWA 
 in real-world ambulatory environments to help identify patients at risk of
  developing these life-threatening conditions. Additionally\, we will prov
 ide insights into the explainability and interpretability of the models us
 ed\, highlighting how these aspects contribute to building trust in AI-dri
 ven diagnostics and ensuring that the predictions are transparent and unde
 rstandable for clinical practitioners.</span></p>
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