INVITED SPEAKER

Cédric Heuchenne holds a Master’s degree in Applied Sciences and a Ph.D. in Statistics from the Catholic University of Louvain and has more than 14 years working as a Statistics/Data Science professor at the University of Liège.

From 2017 until now, he has been the Scientific Advisor at International Research Institute for Artificial Intelligence and Data Science (IAD), Dong A University, Vietnam.

He published more than 40 articles in prestigious peer-reviewed scientific journals (with impact factor and indexed by Scopus), more than 30 articles presented at international conferences, and he has been invited by more than 30 universities for seminars, workshops and research stays. He also participated in writing some chapters of statistics/finance/engineering books, working for two scientific journals as associate editor, and supervised 6 completed Ph.D. Thesis. Besides, Cédric got supported by five important projects funded by Belgian/European funds (more than 400000 euros each) and supervised the whole process from the research content to the management of the research group (he hired more than 20 persons - doctoral students and post-docs -) and the communication with partners, included other universities and companies.

Title: A novel Transformer-based Anomaly Detection approach for ECG Monitoring Healthcare System: a perspective

Abstract: 

Anomaly detection plays a crucial role across various domains, including healthcare, where identifying deviations from normal patterns can help with early intervention and improved outcomes. In healthcare, such as in ECG analysis, detecting abnormal signals is essential for timely diagnosis and treatment, as it can help identify potentially life-threatening conditions that could otherwise go undetected.
In this work, by focusing on ECG anomaly detection as an illustrative healthcare application, we propose to use a transformer-based variational autoencoder network together with a MEWMA-SVDD control chart to achieve anomaly detection. By employing this approach, we can effectively control the false alarm rate to minimize unnecessary alerts. Our proposed framework not only excels in terms of accuracy but also reduces the false alarm rate, making it a favorable choice compared to existing methods.

Researchgate: https://www.researchgate.net/profile/Cedric-Heuchenne