Xi Long is an Associate Professor in the Signal Processing Systems group, where he advises, coordinates and participates in many collaborative projects (with, for example, Philips, Máxima Medical Center, Kempenhaeghe, UMC Utrecht, KU Leuven and Fudan University) combining technology and healthcare.
Long’s expertise is in signal processing, time series analysis, machine learning, deep learning and mathematical modeling. His research interests include engineering for biomedical applications such as unobtrusive sensing, vital signs monitoring, sleep, neonatology & pregnancy, epilepsy & brain activity, and clinical decision support.
I always believe opening my eyes to see different worlds will encourage me to think about things that better our life.”
Xi Long holds a BSc with honor in Electronic Information Engineering from Zhejiang University (China) and obtained his MSc in Electrical Engineering from Eindhoven University of Technology (TU/e) in 2009. He worked at Philips as a researcher from 2008 to 2009 and at Tencent (China) as project manager and UX engineer from 2009 to 2011. He went on to do his PhD at TU/e and Philips Research, on signal processing and machine learning in unobtrusive sleep monitoring, which he completed cum laude in 2015. He then joined Philips in 2016 and is currently a Senior Scientist and Data Analytics Lead at Philips Research.
Long is an IEEE Senior Member, an associate editor of Frontiers in Digital Health, and an editorial board member Health Informatics Journal. He has published over 100 scientific articles and reports, generated more than 15 patents and supervised more than 40 PhD or MSc students. He is a peer reviewer for many leading international journals/conferences in his research areas.
A deep transfer learning approach for wearable sleep stage classification with photoplethysmographyNPJ Digital Medicine (2021)
Wearable sensing and telehealth technology with potential applications in the coronavirus pandemicIEEE Reviews in Biomedical Engineering (2021)
Sleep stage classification from heart-rate variability using long short-term memory neural networksScientific Reports (2019)
EEG analysis of seizure patterns using visibility graphs for detection of generalized seizuresJournal of Neuroscience Methods (2017)
Unobtrusive sleep state measurements in preterm infants - A reviewSleep Medicine Reviews (2017)
Current Educational Activities
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