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pro vyhledávání: '"Chun-Sen Huang"'
Autor:
Meng-Hsuan Liu, Shang-Yu Chien, Ya-Lun Wu, Ting-Hsuan Sun, Chun-Sen Huang, Kai-Cheng Hsu, Liang-Wen Hang
Publikováno v:
BioMedical Engineering OnLine, Vol 23, Iss 1, Pp 1-16 (2024)
Abstract Objective Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinica
Externí odkaz:
https://doaj.org/article/1745b92c843644b88a374967df5b7bf3
Publikováno v:
Kaohsiung Journal of Medical Sciences, Vol 31, Iss 2, Pp 83-89 (2015)
Extensive use of continuous positive airway pressure (CPAP) has positive clinical benefits for most patients with obstructive sleep apnea (OSA). However, patient adherence is a major limiting factor to the effectiveness of CPAP treatment. This study
Externí odkaz:
https://doaj.org/article/bf9044a012834db19af3ff8c3940c2a9
Autor:
Chun-Sen Huang, 黃春森
98
Background: Obstructive sleep apnea syndrome (OSAS) is the most important sleep disorder found in chest and ENT clinics. Continues positive airway pressure (CPAP) is deemed as the gold standard treatment for patients with OSAS. Unfortunatel
Background: Obstructive sleep apnea syndrome (OSAS) is the most important sleep disorder found in chest and ENT clinics. Continues positive airway pressure (CPAP) is deemed as the gold standard treatment for patients with OSAS. Unfortunatel
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/91546489781351811493
Publikováno v:
Sleepbreathing = SchlafAtmung. 25(3)
Low respiratory arousal threshold (ArTH) has been observed to be prevalent in patients with obstructive sleep apnea (OSA), and is associated with poor adherence to continuous positive airway pressure (CPAP) treatment. This study aimed to examine the