T-Wave Alternans Detection with Machine and Deep Learning Techniques in Real Ambulatory Environments
In this lecture, we will focus on the characterization and detection of T-Wave Alternans (TWA), a micro-volt fluctuation in electrocardiogram (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 intelligence techniques, 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 provide insights into the explainability and interpretability of the models used, highlighting how these aspects contribute to building trust in AI-driven diagnostics and ensuring that the predictions are transparent and understandable for clinical practitioners.
Date and Time
Location
Hosts
Registration
- Date: 26 Sep 2024
- Time: 10:00 AM to 11:00 PM
- All times are (UTC+02:00) Zagreb
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- Faculty of Electrical Engineering and Computing
- Unska 3
- Zagreb, Grad Zagreb
- Croatia 10000
- Building: D
- Room Number: D033
- Click here for Map
- Contact Event Hosts
- Co-sponsored by University of Zagreb Faculty of Electrical Engineering and Computing
Speakers
Lidia Pascual Sánchez of Universidad de Alcalá
T-Wave Alternans Detection with Machine and Deep Learning Techniques in Real Ambulatory Environments
In this lecture, we will focus on the characterization and detection of T-Wave Alternans (TWA), a micro-volt fluctuation in electrocardiogram (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 intelligence techniques, 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 provide insights into the explainability and interpretability of the models used, highlighting how these aspects contribute to building trust in AI-driven diagnostics and ensuring that the predictions are transparent and understandable for clinical practitioners.
Biography:
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.
Email:
Address:Universidad de Alcalá, Pza. San Diego, s/n, Alcalá de Henares, Madrid, Spain, 28801
Carmen Plaza Seco of Universidad de Alcalá
T-Wave Alternans Detection with Machine and Deep Learning Techniques in Real Ambulatory Environments
In this lecture, we will focus on the characterization and detection of T-Wave Alternans (TWA), a micro-volt fluctuation in electrocardiogram (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 intelligence techniques, 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 provide insights into the explainability and interpretability of the models used, highlighting how these aspects contribute to building trust in AI-driven diagnostics and ensuring that the predictions are transparent and understandable for clinical practitioners.
Biography:
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.
Email:
Address:Universidad de Alcalá, Pza. San Diego, s/n, Alcalá de Henares, Madrid, Spain, 28801