Dataset Information

Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation.

ABSTRACT: PURPOSE:Sleep apnea and hypopnea syndrome (SAHS) seriously affects sleep quality. In recent years, much research has focused on the detection of SAHS using various physiological signals and algorithms. The purpose of this study is to find an efficient model for detection of apnea-hypopnea events based on nasal flow and SpO2 signals. METHODS:A 60-s detector and a 10-s detector were cascaded for precise detection of apnea-hypopnea (AH) events. Random forests were adopted for classification of data segments based on morphological features extracted from nasal flow and arterial blood oxygen saturation (SpO2). Then the segments' classification results were fed into an event detector to locate the start and end time of every AH event and predict the AH index (AHI). RESULTS:A retrospective study of 24 subjects' polysomnography recordings was conducted. According to segment analysis, the cascading detection model reached an accuracy of 88.3%. While Pearson's correlation coefficient between estimated AHI and reference AHI was 0.99, in the diagnosis of SAHS severity, the proposed method exhibited a performance with Cohen's kappa coefficient of 0.76. CONCLUSIONS:The cascading detection model is able to detect AH events and provide an estimate of AHI. The results indicate that it has the potential to be a useful tool for SAHS diagnosis.

SUBMITTER: Yu H

PROVIDER: S-EPMC7289775 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation.

Yu Hui H Deng Chenyang C Sun Jinglai J Chen Yanjin Y Cao Yuzhen Y

Sleep & breathing = Schlaf & Atmung 20190705 2

<h4>Purpose</h4>Sleep apnea and hypopnea syndrome (SAHS) seriously affects sleep quality. In recent years, much research has focused on the detection of SAHS using various physiological signals and algorithms. The purpose of this study is to find an efficient model for detection of apnea-hypopnea events based on nasal flow and SpO<sub>2</sub> signals.<h4>Methods</h4>A 60-s detector and a 10-s detector were cascaded for precise detection of apnea-hypopnea (AH) events. Random forests were adopted ...[more]

PMID: 31278530

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Project description:ObjectivesThe aim of this study was to identify correlations between sleep apnea severity and tongue volume or posterior airway space measured via three-dimensional reconstruction of volumetric computerized tomography (CT) images in patients with obstructive sleep apnea (OSA) for use in predicting OSA severity and in surgical treatment. We also assessed associations between tongue volume and Mallampati score.MethodsSnoring/OSA male patients (n = 64) who underwent polysomnography, cephalometry, and CT scans were enrolled in this retrospective study. OSA was diagnosed when the apnea-hypopnea index (AHI) was greater than 5 (mild 5-14; moderate 15-29; severe>30). The patients were also categorized into the normal-mild group (n = 22) and the moderate-severe group (n = 42). Using volumetric CT images with the three-dimensional reconstruction technique, the volume of the tongue, posterior airway space volume, and intra-mandibular space were measured. The volumes, polysomnographic parameters, and physical examination findings were compared, and independent factors that are related to OSA were analysed.ResultsNo associations between tongue volume or posterior airway space and the AHI were observed. However, multivariate linear analyses showed that tongue volume had significantly negative association with lowest O2 saturation (r = 0.365, p = 0.027). High BMI was related to an increase in tongue volume. Modified Mallampati scores showed borderline significant positive correlations with absolute tongue volume (r = 0.251, p = 0.046) and standardized tongue volume (absolute tongue volume / intramandibular area; r = 0.266, p = 0.034). Between the normal-mild and moderate-severe groups, absolute tongue volumes were not different, although the standardized tongue volume in the moderate-severe group was significantly higher.ConclusionAbsolute tongue volume showed stronger associations with lowest O2 saturation during sleep than with the severity of AHI. We also found that high BMI was a relevant factor for an increase in absolute tongue volume and modified Mallampati grading was a useful physical examination to predict tongue size.

Project description:BackgroundHigh-flow nasal oxygen therapy (HFNC) may be an attractive first-line ventilatory support in COVID-19 patients. However, HNFC use for the management of COVID-19 patients and risk factors for HFNC failure remain to be determined.MethodsIn this retrospective study, we included all consecutive COVID-19 patients admitted to our intensive care unit (ICU) in the first (Mars-May 2020) and second (August 2020- February 202) French pandemic waves. Patients with limitations for intubation were excluded. HFNC failure was defined as the need for intubation after ICU admission. The impact of HFNC use was analyzed in the whole cohort and after constructing a propensity score. Risk factors for HNFC failure were identified through a landmark time-dependent cause-specific Cox model. The ability of the 6-h ROX index to detect HFNC failure was assessed by generating receiver operating characteristic (ROC) curve.Results200 patients were included: HFNC was used in 114(57%) patients, non-invasive ventilation in 25(12%) patients and 145(72%) patients were intubated with a median delay of 0 (0-2) days after ICU admission. Overall, 78(68%) patients had HFNC failure. Patients with HFNC failure had a higher ICU mortality rate (34 vs. 11%, p = 0.02) than those without. At landmark time of 48 and 72 h, SAPS-2 score, extent of CT-Scan abnormalities > 75% and HFNC duration (cause specific hazard ratio (CSH) = 0.11, 95% CI (0.04-0.28), per + 1 day, p < 0.001 at 48 h and CSH = 0.06, 95% CI (0.02-0.23), per + 1 day, p < 0.001 at 72 h) were associated with HFNC failure. The 6-h ROX index was lower in patients with HFNC failure but could not reliably predicted HFNC failure with an area under ROC curve of 0.65 (95% CI(0.52-0.78), p = 0.02). In the matched cohort, HFNC use was associated with a lower risk of intubation (CSH = 0.32, 95% CI (0.19-0.57), p < 0.001).ConclusionsIn critically-ill COVID-19 patients, while HFNC use as first-line ventilatory support was associated with a lower risk of intubation, more than half of patients had HFNC failure. Risk factors for HFNC failure were SAPS-2 score and extent of CT-Scan abnormalities > 75%. The risk of HFNC failure could not be predicted by the 6-h ROX index but decreased after a 48-h HFNC duration.

Dataset Information

Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation.

Publications

Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation.

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