At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch.
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ABSTRACT:
SUBMITTER: Zavanelli N
PROVIDER: S-EPMC8694628 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionObstructive sleep apnea (OSA) has been associated with non-dipping blood pressure (BP). The precise mechanism is still under investigation, but repetitive oxygen desaturation and arousal induced sleep fragmentation are considered the main contributors.MethodsWe analyzed beat-to-beat measurements of hemodynamic parameters (HPs) during a 25-min period of wake-sleep transition. Differences in the mean HP values for heart rate (HR), systolic BP (SBP), and stroke volume (SV) during wake and sleep and their standard deviations (SDs) were compared between 34 controls (C) and 22 OSA patients. The Student's t-test for independent samples and the effect size by Cohen's d (d) were calculated. HP evolution was investigated by plotting the measured HP values against each consecutive pulse wave. After a simple regression analysis, the calculated coefficient beta (SCB) was used to indicate the HP evolution. We furthermore explored by a hierarchical block regression which variables increased the prediction for the SCB: model 1 BMI and age, model 2 + apnea/hypopnea index (AHI), and model 3 + arousal index (AI).ResultsBetween the two groups, the SBP increased in OSA and decreased in C resulting in a significant difference (p = 0.001; d = 0.92). The SV demonstrated a similar development (p = 0.047; d = 0.56). The wake/sleep variation of the HP measured by the SD was higher in the OSA group-HR: p < 0.001; d = 1.2; SBP: p = 0.001; d = 0.94; and SV: p = 0.005; d = 0.82. The hierarchical regression analysis of the SCB demonstrated in SBP that the addition of AI to AHI resulted in ?R 2: +0.163 and ?F + 13.257 (p = 0.001) and for SV ?R 2: +0.07 and ?F 4.83 (p = 0.003). The AI but not the AHI remained statistically significant in the regression analysis model 3-SBP: ? = 0.717, p = 0.001; SV: ? = 0.469, p = 0.033.ConclusionIn this study, we demonstrated that in OSA, the physiological dipping in SBP and SV decreased, and the variation of all investigated parameters increased. Hierarchical regression analysis indicates that the addition of the AI to BMI, age, and AHI increases the prediction of the HP evolution following sleep onset for both SBP and SV and may be the most important variable.
| S-EPMC7082422 | biostudies-literature
Project description:Sleep is an essential element to human life, restoring the brain and body from accumulated fatigue from daily activities. Quantitative monitoring of daily sleep quality can provide critical feedback to evaluate human health and life patterns. However, the existing sleep assessment system using polysomnography is not available for a home sleep evaluation, while it requires multiple sensors, tabletop electronics, and sleep specialists. More importantly, the mandatory sleep in a designated lab facility disrupts a subject's regular sleep pattern, which does not capture one's everyday sleep behaviors. Recent studies report that galvanic skin response (GSR) measured on the skin can be one indicator to evaluate the sleep quality daily at home. However, the available GSR detection devices require rigid sensors wrapped on fingers along with separate electronic components for data acquisition, which can interrupt the normal sleep conditions. Here, we report a new class of materials, sensors, electronics, and packaging technologies to develop a wireless, soft electronic system that can measure GSR on the wrist. The single device platform that avoids wires, rigid sensors, and straps offers the maximum comfort to wear on the skin and minimize disruption of a subject's sleep. A nanomaterial GSR sensor, printed on a soft elastomeric membrane, can have intimate contact with the skin to reduce motion artifact during sleep. A multi-layered flexible circuit mounted on top of the sensor provides a wireless, continuous, real-time recording of GSR to classify sleep stages, validated by the direct comparison with the standard method that measures other physiological signals. Collectively, the soft bioelectronic system shows great potential to be working as a portable, at-home sensor system for assessing sleep quality before a hospital visit.
| S-EPMC7825679 | biostudies-literature
Project description:Study Objectives:To determine whether defining two subtypes of sleep-disordered breathing (SDB) events-with or without hypoxia-results in measures that are more strongly associated with hypertension and sleepiness. Methods:A total of 1022 participants with 2112 nocturnal polysomnograms from the Wisconsin Sleep Cohort were analyzed with our automated algorithm, developed to detect breathing disturbances and desaturations. Breathing events were time-locked to desaturations, resulting in two indices-desaturating (hypoxia-breathing disturbance index [H-BDI]) and nondesaturating (nonhypoxia-breathing disturbance index [NH-BDI]) events-regardless of arousals. Measures of subjective (Epworth Sleepiness Scale) and objective (2981 multiple sleep latency tests from a subset of 865 participants) sleepiness were analyzed, in addition to clinically relevant clinicodemographic variables. Hypertension was defined as BP ? 140/90 or antihypertensive use. Results:H-BDI, but not NH-BDI, correlated strongly with SDB severity indices that included hypoxia (r ? 0.89, p ? .001 with 3% oxygen-desaturation index [ODI] and apnea hypopnea index with 4% desaturations). A doubling of desaturation-associated events was associated with hypertension prevalence, which was significant for ODI but not H-BDI (3% ODI OR = 1.06, 95% CI = 1.00-1.12, p < .05; H-BDI OR 1.04, 95% CI = 0.98-1.10) and daytime sleepiness (? = 0.20 Epworth Sleepiness Scale [ESS] score, p < .0001; ? = -0.20 minutes in MSL on multiple sleep latency test [MSLT], p < .01). Independently, nondesaturating event doubling was associated with more objective sleepiness (? = -0.52 minutes in MSL on MSLT, p < .001), but had less association with subjective sleepiness (? = 0.12 ESS score, p = .10). In longitudinal analyses, baseline nondesaturating events were associated with worsening of H-BDI over a 4-year follow-up, suggesting evolution in severity. Conclusions:In SDB, nondesaturating events are independently associated with objective daytime sleepiness, beyond the effect of desaturating events.
| S-EPMC6251661 | biostudies-literature
Project description:This study examines the relationship between sleep apnea and glucose metabolism. Physiological studies have demonstrated that 5 days of exposure to intermittent hypoxia (similar to what occurs with sleep apnea) leads to significant improvements in glucose tolerance. Therefore, this study investigates the hypothesis that intermittent hypoxia may lead to upregulation of some novel peptide(s) that have a powerful glucose lowering action. Keywords: other
2003-07-16 | GSE480 | GEO
Project description:ObjectiveTo evaluate sleep disturbances in a diverse, contemporary HIV-positive patient cohort and to identify demographic, clinical, and immune correlates.MethodsA convenience sample of 176 patients from a racially and ethnically diverse HIV-positive patient cohort in an urban population. This was a cross-sectional, epidemiologic study. We surveyed participants using multiple standardized instruments to assess depression, sleep quality, and risk for sleep apnea. We analyzed demographic, behavioral, and clinical correlates.ResultsA total of 56% of participants were female, 75% Black and 64% had heterosexual HIV risk. The median age was 49?years. Poor sleep quality (Pittsburgh Sleep Quality Index?>?5) was reported by 73% of patients and 52% met insomnia diagnosis criteria. A single question about self-reported sleep problems predicted a Pittsburgh Sleep Quality Index?>?5 with a sensitivity and specificity of 82% and 81%, respectively. Female sex was significantly associated with higher risk of poor sleep quality, depression, and insomnia, whereas higher risk of obstructive sleep apnea was significantly associated with older age, male sex, obesity (body mass index???30?kg/m2), and metabolic comorbidities. High risk for obstructive sleep apnea, high rate of depression, and poor sleep hygiene represent treatment targets for sleep problems in HIV patients.ConclusionSleep disturbances were common in this patient cohort, although largely undiagnosed and untreated. Sleep problems are linked to worse disease progression and increased cardiovascular mortality. Screening for sleep problems with a single question had high sensitivity and specificity. In those patients with self-reported sleep problems, screening for obstructive sleep apnea, depression, and sleep hygiene habits should be part of routine HIV care.
| S-EPMC6454647 | biostudies-literature
Project description:STUDY OBJECTIVES:Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries information about apnea/hypopnea and sleep/wake status. We hypothesized that image analysis of all-night TS recordings by a deep neural network (DNN) would be capable of detecting breathing events and classifying sleep/wake status. The aim of this study is to develop a DNN-based system for sleep apnea testing and validate it using a large sampling of polysomnography (PSG) data. METHODS:PSG examinations for the evaluation of sleep-disordered breathing (SDB) were performed for 1,852 patients: 1,548 PSG records were used to develop the system, and the remaining 304 records were used for validation. TS spectrogram images were obtained every 60 seconds and labeled with the PSG scoring results (breathing event and sleep/wake status), then introduced to DNN learning. Two different DNNs were trained for breathing status and sleep/wake status, respectively. RESULTS:A DNN with convolutional layers showed the best performance for discriminating breathing status. The same DNN structure was trained for sleep/wake discrimination. In the validation study, the DNN analysis was capable of discriminating the sleep/wake status with reasonable accuracy. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic curves for diagnosis of SDB with apnea-hypopnea index of > 5, 15, and 30 were 0.98, 0.76, and 0.99; 0.97, 0.90, and 0.99; and 0.92, 0.94, and 0.98, respectively. CONCLUSIONS:The developed system using the TS DNN analysis has a good performance for SDB testing. CITATION:Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(8): 1125-1133.
| S-EPMC6707047 | biostudies-literature
Project description:We hypothesized increasing obstructive sleep apnea (OSA) severity would be associated with nondipping blood pressure (BP) in increased cardiovascular disease (CVD) risk.Baseline data from 298 cardiology patients recruited for a multicenter randomized controlled trial were examined. Dipping was defined as a sleep-related BP or heart rate (HR) reduction of at least 10%. Logistic regression models were fit, adjusting for age, sex, race, BMI, CVD risk factors, CVD, and study site.There was a statistically significant 4% increase in the odds of nondipping SBP per 1-unit increase in both apnea hypopnea index (AHI) and oxygen desaturation index (ODI). There was no significant relationship between AHI and nondipping mean arterial pressure (MAP); however, a 3% increase in the odds of nondipping MAP per 1-unit increase in ODI was observed [odds ratio (OR) = 1.03; 95% confidence interval (CI) 1.00-1.05]. At severe OSA levels, a 10 and 4% increase in odds of nondipping DBP per 1-unit increase in AHI and ODI were observed, respectively. A 6% [OR = 1.06; 95% CI (1.01-1.10)] increase in nondipping HR odds was observed with each increase in ODI until the upper quartile of ODI.In patients at cardiovascular risk and moderate-to-severe OSA, increasing AHI and/or ODI were associated with increased odds of nondipping SBP and nondipping MAP. More severe levels of AHI and ODI also were associated with nondipping DBP. These results support progressive BP burden associated with increased OSA severity even in patients managed by cardiology specialty care.
| S-EPMC4096765 | biostudies-literature
Project description:Rationale: Obstructive sleep apnea (OSA) has been associated with metabolic dysregulation and systemic inflammation. This may be due to pathophysiologic effects of OSA on visceral adipose tissue. We sought to assess the transcriptional consequences of OSA on adipocytes by utilizing pathway-focused analyses. Methods: Patients scheduled to undergo ventral hernia repair surgery were recruited to wear a portable home sleep monitor for two nights prior to surgery. Visceral fat biopsies were obtained intra-operatively. RNA was extracted and whole-genome expression profiling was performed. Gene Set Enrichment Analysis (GSEA) was used to identify curated gene sets that were differentially enriched in OSA subjects. Network analysis was applied to a select set of highly enriched pathways. Results: 10 patients with OSA and 8 control subjects were recruited. There were no differences in age, gender, body mass index between the two groups, but the OSA subjects had a significantly higher respiratory disturbance index (19.2 vs. 0.6, P-value 0.05) and worse hypoxemia (minimum oxygen saturation 79.7% vs. 87.8%, P-value < 0.001). GSEA identified a number of gene sets up-regulated in adipose tissue of OSA patients including the pro-inflammatory NF-M-NM-:B pathway and the proteolytic ubiquitin/proteasome module. A critical metabolic pathway, the peroxisome proliferator-activated receptor (PPAR), was down-regulated in subjects with OSA. Network analysis linked members of these modules together and identified regulatory hubs. Conclusions: OSA is associated with alterations in visceral fat gene expression. Pathway-based network analysis highlighted perturbations in several key pathways whose coordinated interactions may contribute to the metabolic dysregulation observed in this complex disorder. Total RNA from visceral fat of 18 subjects (10 OSA, 8 Control) was hybridized to 18 Affymetrix Genechip Human Gene 1.0 ST microarrays.
2012-09-01 | E-GEOD-38792 | biostudies-arrayexpress
Project description:Rationale: Obstructive sleep apnea (OSA) has been associated with metabolic dysregulation and systemic inflammation. This may be due to pathophysiologic effects of OSA on visceral adipose tissue. We sought to assess the transcriptional consequences of OSA on adipocytes by utilizing pathway-focused analyses. Methods: Patients scheduled to undergo ventral hernia repair surgery were recruited to wear a portable home sleep monitor for two nights prior to surgery. Visceral fat biopsies were obtained intra-operatively. RNA was extracted and whole-genome expression profiling was performed. Gene Set Enrichment Analysis (GSEA) was used to identify curated gene sets that were differentially enriched in OSA subjects. Network analysis was applied to a select set of highly enriched pathways. Results: 10 patients with OSA and 8 control subjects were recruited. There were no differences in age, gender, body mass index between the two groups, but the OSA subjects had a significantly higher respiratory disturbance index (19.2 vs. 0.6, P-value 0.05) and worse hypoxemia (minimum oxygen saturation 79.7% vs. 87.8%, P-value < 0.001). GSEA identified a number of gene sets up-regulated in adipose tissue of OSA patients including the pro-inflammatory NF-κB pathway and the proteolytic ubiquitin/proteasome module. A critical metabolic pathway, the peroxisome proliferator-activated receptor (PPAR), was down-regulated in subjects with OSA. Network analysis linked members of these modules together and identified regulatory hubs. Conclusions: OSA is associated with alterations in visceral fat gene expression. Pathway-based network analysis highlighted perturbations in several key pathways whose coordinated interactions may contribute to the metabolic dysregulation observed in this complex disorder.
2012-09-01 | GSE38792 | GEO
Project description:Intermittent hypoxia and sleep fragmentation are pathophysiological processes involved in obstructive sleep apnea (OSA) which affect gut microbiota, sleep architecture, and mTOR signaling pathway. However, the involvement of these elements in the pathogenesis mechanism of OSA-associated hypertension remains unclear. Therefore, this study investigated whether the OSA-associated hypertension mechanism is regulated by the gut microbiota and mTOR signaling pathway. Patients were diagnosed by polysomnography; their fecal samples were obtained and analyzed for their microbiome composition by 16S ribosomal RNA pyrosequencing and bioinformatics analysis. Transcript genes on fasting peripheral blood mononuclear cells (PBMCs) were examined using Illumina RNA-sequencing analysis. Totally, we enrolled 60 patients with severe OSA [without hypertension (n = 27) and with hypertension (n = 33)] and 12 controls (neither OSA nor hypertension). Results revealed that severe-OSA patients with hypertension had an altered gut microbiome, decreased short-chain fatty acid-producing bacteria (P < 0.05), and reduced arginine and proline metabolism pathways (P=0.001), compared with controls; also, they had increased stage N1 sleep and reduced stages N2 and N3 sleep accompanied by repeated arousals (P < 0.05). Analysis of PBMCs using the Kyoto Encyclopedia of Genes and Genomes database showed that the mTOR signaling pathway (P=0.006) was the most important differential gene-enriched pathway in severe-OSA patients with hypertension. Our findings extend prior work and suggest a possibility that the regulation of the mTOR signaling pathway is involved in developing OSA-associated hypertension through its interaction with the disturbance of the gut microbiome and sleep architecture.
| S-EPMC8651365 | biostudies-literature