Physiological measurement
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Physiological measurement · Apr 2016
Apnea-hypopnea index estimation using quantitative analysis of sleep macrostructure.
Obstructive sleep apnea, characterized by recurrent cessation or substantial reduction in breathing during sleep, is a prevalent and serious medical condition. Although a significant relationship between obstructive sleep apnea and sleep macrostructure has been revealed in several studies, useful applications of this relationship have been limited. The aim of this study was to suggest a novel approach using quantitative analysis of sleep macrostructure to estimate the apnea-hypopnea index, which is commonly used to assess obstructive sleep apnea. ⋯ Between the apnea-hypopnea index estimates and the reference values reported from polysomnography, a root mean square error of 7.30 events h(-1) was obtained in the validation set. At an apnea-hypopnea index cut-off of ⩾30 events h(-1), the obstructive sleep apnea diagnostic performance was provided with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 92.4% by our method. The developed apnea-hypopnea index estimation model has the potential to be utilized in circumstances in which it is not possible to acquire or analyze respiration signal but it is possible to obtain information on sleep macrostructure.
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Physiological measurement · Apr 2016
Pattern discovery in critical alarms originating from neonates under intensive care.
Patient monitoring generates a large number of alarms, the vast majority of which are false. Excessive non-actionable medical alarms lead to alarm fatigue, a well-recognized patient safety issue. While multiple approaches to reduce alarm fatigue have been explored, patterns in alarming and inter-alarm relationships, as they manifest in the clinical workspace, are largely a black-box and hamper research efforts towards reducing alarms. ⋯ Examination of the prevalent alarm sequences reveals that while many sequences comprised of multiple alarms, nearly 65% of the sequences were isolated instances of alarms and are potentially irreducible. Patterns in alarming, as they manifest in the clinical workspace were identified and visualized. This information can be exploited to investigate strategies for reducing alarms.