BreathFinder: A Method for Non-Invasive Isolation of Respiratory Cycles Utilizing the Thoracic Respiratory Inductance Plethysmography Signal

Benedikt Holm,1 Michal Borsky,1 Erna S Arnardottir,2,3 Marta Serwatko,2 Jacky Mallett,1 Anna Sigridur Islind,1 María Óskarsdóttir1 1Reykjavik University, School of Technology, Department of Computer Science, Reykjavik, Iceland; 2Reykjavik University, School of Technology, Sleep Institute, Reykjavik, Iceland; 3Landspitali, The National University Hospital of Iceland, Reykjavik, IcelandCorrespondence: Benedikt Holm, Email [email protected]: The field of automatic respiratory analysis focuses mainly on breath detection on signals such as audio recordings, or nasal flow measurement, which suffer from issues with background noise and other disturbances. Here we introduce a novel algorithm designed to isolate individual respiratory cycles on a thoracic respiratory inductance plethysmography signal using the non-invasive signal of the respiratory inductance plethysmography belts.Purpose: The algorithm locates breaths using signal processing and statistical methods on the thoracic respiratory inductance plethysmography belt and enables the analysis of sleep data on an individual breath level.Patients and Methods: The algorithm was evaluated against a cohort of 31 participants, both healthy and diagnosed with obstructive sleep apnea. The dataset consisted of 13 female and 18 male participants between the ages of 20 and 69. The algorithm was evaluated on 7.3 hours of hand-annotated data from the cohort, or 8782 individual breaths in total. The algorithm was specifically evaluated on a dataset containing many sleep-disordered breathing events to confirm that it did not suffer in terms of accuracy when detecting breaths in the presence of sleep-disordered breathing. The algorithm was also evaluated across many participants, and we found that its accuracy was consistent across people. Source code for the algorithm was made public via an open-source Python library.Results: The proposed algorithm achieved an estimated 94% accuracy when detecting breaths in respiratory signals while producing false positives that amount to only 5% of the total number of detections. The accuracy was not affected by the presence of respiratory related events, such as obstructive apneas or snoring.Conclusion: This work presents an automatic respiratory cycle algorithm suitable for use as an analytical tool for research based on individual breaths in sleep recordings that include respiratory inductance plethysmography.Keywords: respiratory analysis, breath detection algorithm, sleep analysis, breath segmentation, respiratory cycle isolatio

Response to Therapeutic Sleep Deprivation: A Naturalistic Study of Clinical and Genetic Factors and Post-treatment Depressive Symptom Trajectory

Research has shown that therapeutic sleep deprivation (SD) has rapid antidepressant effects in the majority of depressed patients. Investigation of factors preceding and accompanying these effects may facilitate the identification of the underlying biological mechanisms. This exploratory study aimed to examine clinical and genetic factors predicting response to SD and determine the impact of SD on illness course. Mood during SD was also assessed via visual analogue scale. Depressed inpatients (n = 78) and healthy controls (n = 15) underwent ~36 h of SD. Response to SD was defined as a score of ≤ 2 on the Clinical Global Impression Scale for Global Improvement. Depressive symptom trajectories were evaluated for up to a month using self/expert ratings. Impact of genetic burden was calculated using polygenic risk scores for major depressive disorder. In total, 72% of patients responded to SD. Responders and non-responders did not differ in baseline self/expert depression symptom ratings, but mood differed. Response was associated with lower age (p = 0.007) and later age at life-time disease onset (p = 0.003). Higher genetic burden of depression was observed in non-responders than healthy controls. Up to a month post SD, depressive symptoms decreased in both patients groups, but more in responders, in whom effects were sustained. The present findings suggest that re-examining SD with a greater focus on biological mechanisms will lead to better understanding of mechanisms of depression

Glucose intolerance and gestational diabetes risk in relation to sleep duration and snoring during pregnancy: a pilot study

<p>Abstract</p> <p>Background</p> <p>Insufficient sleep and poor sleep quality, considered endemic in modern society, are associated with obesity, impaired glucose tolerance and diabetes. Little, however, is known about the consequences of insufficient sleep and poor sleep quality during pregnancy on glucose tolerance and gestational diabetes.</p> <p>Methods</p> <p>A cohort of 1,290 women was interviewed during early pregnancy. We collected information about sleep duration and snoring during early pregnancy. Results from screening and diagnostic testing for gestational diabetes mellitus (GDM) were abstracted from medical records. Generalized linear models were fitted to derive relative risk (RR) and 95% confidence intervals (95% CIs) of GDM associated with sleep duration and snoring, respectively.</p> <p>Results</p> <p>After adjusting for maternal age and race/ethnicity, GDM risk was increased among women sleeping ≤ 4 hours compared with those sleeping 9 hours per night (RR = 5.56; 95% CI 1.31-23.69). The corresponding RR for lean women (<25 kg/m²) was 3.23 (95% CI 0.34-30.41) and 9.83 (95% CI 1.12-86.32) for overweight women (≥ 25 kg/m²). Overall, snoring was associated with a 1.86-fold increased risk of GDM (RR = 1.86; 95% CI 0.88-3.94). The risk of GDM was particularly elevated among overweight women who snored. Compared with lean women who did not snore, those who were overweight and snored had a 6.9-fold increased risk of GDM (95% CI 2.87-16.6).</p> <p>Conclusions</p> <p>These preliminary findings suggest associations of short sleep duration and snoring with glucose intolerance and GDM. Though consistent with studies of men and non-pregnant women, larger studies that include objective measures of sleep duration, quality and apnea are needed to obtain more precise estimates of observed associations.</p

Salivary Cytokines and the Association Between Obstructive Sleep Apnea Syndrome and Periodontal Disease

WOS: 000339684000004PubMed ID: 24410293Background: A higher prevalence of periodontal disease has been reported in patients with obstructive sleep apnea syndrome (OSAS), and these two chronic conditions may be linked via inflammatory pathways. The aim of the present study is to evaluate the salivary interleukin (IL)-1 beta, IL-6, IL-21, IL-33, and pentraxin-3 (PTX3) concentrations in patients with and without OSAS. Methods: A total of 52 patients were included in the study. Thirteen individuals were in the control (non-OSAS) group, 17 were in the mild/moderate OSAS group, and 22 were in the severe OSAS group. Clinical periodontal measurements were recorded, and saliva samples were obtained before initiation of periodontal intervention. Enzyme-linked immunosorbent assay was used to determine salivary cytokine concentrations. Data were statistically analyzed using D'Agostino-Pearson omnibus normality, Spearman rho rank, Kruskal-Wallis, and Dunn tests. Results: Salivary IL-6 and IL-33 concentrations were similar in the two OSAS groups (P >0.05), which were statistically higher than the control group (P <0.05). IL-1 beta, IL-21, and PTX3 concentrations were similar in the study groups. The only significant correlation between clinical periodontal parameters and salivary cytokines was found between clinical attachment level (CAL) and IL-21 (P = 0.02). Highly significant correlations were found between probing depth, CAL measures, and indicators of OSAS severity (P <0.01). Conclusions: The present findings suggest that OSAS may have an increasing effect on salivary IL-6 and IL-33 concentrations regardless of OSAS severity. Additional investigation is required to elucidate a potential bidirectional relationship between OSAS and periodontal disease