Xi Long

Associate Professor

Eindhoven University of Technology
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Netherlands

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Dr. Xi Long is an Associate Professor at Eindhoven University of Technology in the Netherlands. His research primarily focuses on biomedical signal processing, with recent work emphasizing detection methods for conditions such as apnea in preterm infants and sleep disorders. He also explores innovative monitoring techniques utilizing deep learning and sensor technology to enhance health outcomes. His contributions to the field include applications in atrial fibrillation detection and predicting preterm birth through medical record analysis.

Articles (30)

Noncontact respiration monitoring techniques in young children: A scoping review

Pediatric sleep‐related breathing disorders, or sleep‐disordered breathing (SDB), cover a range of conditions, including obstructive sleep apnea, central sleep apnea, sleep‐related hypoventilation disorders, and sleep‐related hypoxemia disorder. Pediatric SDB is often underdiagnosed, potentially due to difficulties associated with performing the gold standard polysomnography in children. This scoping review aims to: (1) provide an overview of the studies reporting on safe, noncontact monitoring of respiration in young children, (2) describe the accuracy of these techniques, and (3) highlight their respective advantages and limitations. PubMed and EMBASE were searched for studies researching techniques in children <12 years old. Both quantitative data and the quality of the studies were analyzed. The evaluation of study quality was conducted using the QUADAS‐2 tool. A total of 19 studies were included. Techniques could be grouped into bed‐based methods, microwave radar, video, infrared (IR) cameras, and garment‐embedded sensors. Most studies either measured respiratory rate (RR) or detected apneas; n = 2 aimed to do both. At present, bed‐based approaches are at the forefront of research in noncontact RR monitoring in children, boasting the most sophisticated algorithms in this field. Yet, despite extensive studies, there remains no consensus on a definitive method that outperforms the rest. The accuracies reported by these studies tend to cluster within a similar range, indicating that no single technique has emerged as markedly superior. Notably, all identified methods demonstrate capability in detecting body movements and RR, with reported safety for use in children across the board. Further research into contactless alternatives should focus on cost‐effectiveness, ease‐of‐use, and widespread availability.

Year:

2024

Collaborators (16)

Sebastiaan Overeem

Eindhoven University of Technology

NETHERLANDS

Ronald Aarts

Eindhoven University of Technology

NETHERLANDS

Mengzhu XU

Eindhoven University of Technology

NETHERLANDS

Peter Andriessen

Eindhoven University of Technology

NETHERLANDS

Johannes van Dijk

Eindhoven University of Technology

NETHERLANDS

Gabriela Maria Grońska

Eindhoven University of Technology

NETHERLANDS

Zheng Peng

Eindhoven University of Technology

NETHERLANDS

Massimo Mischi

Professor at Eindhoven University of Technology

Eindhoven University of Technology

NETHERLANDS

Heenam Yoon

Sangmyung University

SOUTH KOREA

Rong-Hao Liang

Assistant Professor

Eindhoven University of Technology

NETHERLANDS

Gabriele Varisco

Eindhoven University of Technology

NETHERLANDS

Jeroen Dudink

-

NETHERLANDS

Qinhao Wu

Leiden University

NETHERLANDS

Sang Ho Choi

Assistant Professor

Kwangwoon University

SOUTH KOREA

M. Beatrijs van der Hout-van der Jagt

Eindhoven University of Technology

NETHERLANDS

Haipeng Liu

Coventry University

UNITED KINGDOM
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