Prijava

Poziv na radionicu "Workshop on Machine Learning and Manifold Learning Models for Cardiac Signals"

Zavod za elektroničke sustave i obradbu informacija, Odjel za tehniku u medicini i biologiji (EMB18) i Odjel za obradu signala (SP01) Hrvatske sekcije IEEE pozivaju vas na radionicu organiziranu u sklopu programa razmjene Erasmus KA131+ pod naslovom

Workshop on Machine Learning and Manifold Learning Models for Cardiac Signals

kojeg će održati

MSc Carmen Plaza Seco i MSc Lidia Pascual Sánchez sa Sveučilišta u Alcali, Španjolska.

Radionica će se održati na Fakultetu elektrotehnike i računarstva Sveučilišta u Zagrebu u

četvrtak 3. listopada 2024. godine u 10:00 sati u A102.

Radionica je na engleskom jeziku, a predviđeno trajanje je 120 minuta.

Radionica je otvorena za sve zainteresirane, a posebno pozivamo studente, no zbog ograničenog broja mjesta potrebno se

prijaviti za sudjelovanje.

Sažetak radionice i kratki životopisi predavačica su dostupni u opširnijoj obavijesti.

Sažetak radionice

This workshop will start with an introduction to the fundamentals of artificial intelligence. Participants will work interactively with a runnable script, applying machine learning and manifold learning techniques to ECG signals to detect T-Wave Alternans (TWA), a micro-volt fluctuation considered a biomarker for cardiac conditions such as sudden cardiac death. The session will be conducted using Python via Google Colab.

Životopis predavačica:

Carmen Plaza Seco is a PhD student at the University of Alcalá, Madrid, Spain. She graduated in Biomedical Engineering in 2020 and completed her MSc in Computational Intelligence and Interactive Systems at the Autonomous University of Madrid in 2021. Her current research focuses on the development of learning-based techniques for the characterization of long-term electrocardiographic signals. She is also collaborating with the University of Delaware in the United States as part of her PhD work.

Lidia Pascual Sánchez is a PhD student at the University of Alcalá, Madrid, Spain. She graduated in Biomedical Engineering in 2021 and completed her MSc in Machine Learning in Health from Carlos III University of Madrid in 2022. Her doctoral research centers on developing learning-based methods for detecting T-Wave Alternans in ambulatory electrocardiographic signals.