X
Xi Long
Researcher at Eindhoven University of Technology
Publications - 141
Citations - 2714
Xi Long is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Polysomnography & Computer science. The author has an hindex of 24, co-authored 123 publications receiving 1957 citations. Previous affiliations of Xi Long include Columbia University & Central South University.
Papers
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Proceedings ArticleDOI
Single-accelerometer-based daily physical activity classification
Xi Long,Bin Yin,Ronald M. Aarts +2 more
TL;DR: This study compared a Bayesian classification with that of a Decision Tree based approach, finding a Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities.
Journal ArticleDOI
Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic
Xiaorong Ding,David A. Clifton,Nan Ji,Nigel H. Lovell,Paolo Bonato,Wei Chen,Xinge Yu,Zhong Xue,Ting Xiang,Xi Long,Ke Xu,Xinyu Jiang,Qi Wang,Bin Yin,Guodong Feng,Yuan-Ting Zhang +15 more
TL;DR: Enable technologies and systems suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals are reviewed.
Journal ArticleDOI
An Active Power-Decoupling Method for Single-Phase AC–DC Converters
TL;DR: To enhance the power-decoupling performance, a direct ripple power-cancellation method based on energy-storage inductor is proposed and a multiresonant controller with feed-forward control is introduced for fast and precise current tracking.
Journal ArticleDOI
Sleep stage classification with ECG and respiratory effort.
Pedro Fonseca,Xi Long,Xi Long,Mustafa Radha,Reinder Haakma,Ronald M. Aarts,Ronald M. Aarts,J Jérôme Rolink +7 more
TL;DR: A methodology for classifying wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) light and deep sleep on a 30 s epoch basis and achieving a Cohen's kappa coefficient of 0.49 and an accuracy of 69% in the classification of wake, REM, light, anddeep sleep is described.
Journal ArticleDOI
Sleep stage classification from heart-rate variability using long short-term memory neural networks
Mustafa Radha,Pedro Fonseca,Pedro Fonseca,Arnaud Moreau,Marco Ross,Andreas Cerny,Peter Anderer,Xi Long,Xi Long,Ronald M. Aarts,Ronald M. Aarts +10 more
TL;DR: A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set, demonstrating the merit of deep temporal modelling using a diverse data set and advancing the state-of-the-art for HRV-based sleep stage classification.