RESUMEN

The community population based studies on the relationship between obstructive sleep apnea and liver injury are limited. The study aimed to clarify the association between sleep apnea (SA) and liver injury by using the data in The National Health and Nutrition Examination Survey. SA was assessed by the sleep questionnaire and liver injury was evaluated by liver function test, hepatic steatosis index, and fibrosis-4. Weighted multivariable linear regression was performed to examine the association between SA and liver injury. Subgroup analyses and sensitivity analysis were also conducted. A total of 19,362 eligible participants were included in the study. After adjusting for confounders, the presence of SA was significantly associated with increased levels of lnALT, lnAST/alanine aminotransferase, lnGGT, and lnHSI (all P values < .05), but not with lnFIB-4 (P > .05). There is a dose-response relationship between the severity of SA and increased levels of lnALT, lnGGT, and decreased levels of lnAST/alanine aminotransferase (test for trend, all P values < .05). Subgroup analyses revealed that the positive association between SA and liver function, liver steatosis showed a tendency to exist in nonobese, younger, non-Hispanic Black, and male populations. Sensitive analysis showed the relationship between SA and liver injury was stable. Self-reported SA was independently associated with elevated liver enzymes and liver steatosis among US population. The association was more pronounced among nonobese, younger, non-Hispanic Black, and male populations.

Asunto(s)

Asunto(s)

RESUMEN

BACKGROUND: Sleep-disordered breathing (SDB) and allergic rhinitis (AR) are common problems that can lead to worsening quality of life (QOL) in children with these conditions. There is scarce evidence on the QOL of children with SDB outside of the hospital setting with inconsistent evidence on the association of AR and QOL concerning the SDB in children. Thus, the primary objective of this study is to determine the QOL concerning the SDB of elementary school students by using OSA-18. We also aim to provide the relationship of allergic rhinitis to the QOL. METHODS: A cross-sectional study was conducted on all elementary school students, aged 6-12 years, from 10 elementary schools. The QOL of all participants was evaluated by the Thai version of the caregiver-administered OSA-18 questionnaire. The simple and multiple linear regression models were used to determine the effect of allergic rhinitis on the OSA-18 total scores. RESULTS: A total of 3,053 children were included in the final analysis, 50.1% male. At least a moderate impact on QOL from SDB was observed in 9.4% of the population. Children with AR had significantly higher mean total OSA- 18 scores than the children without AR (47.5 ± 15.0 VS 38.5 ± 13.1, p < 0.001). After the adjustment for age, gender, body mass index, household income, and history of asthma, the point estimate of the adjusted beta regression coefficient on the OSA-18 total score in children with AR was 7.82 (95% CI: 6.00-9.65, p < 0.001). Significant associations were observed between AR and all domains except for emotional distress. CONCLUSIONS: A substantial number of elementary school children had at least a moderate impact on the QOL from SDB, especially those with AR. Thus, effective screening of SDB in children with AR should be done to improve the QOL of these children.

Asunto(s)

RESUMEN

BACKGROUND: Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. OBJECTIVE: The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. METHODS: Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. RESULTS: Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. CONCLUSIONS: Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.

Asunto(s)

RESUMEN

BACKGROUND: Sleep apnea (SA) has been linked to an increased risk of dementia in numerous observational studies; whether this is driven by neurodegenerative, vascular, or other mechanisms is not clear. We sought to examine the bidirectional causal relationships between SA, Alzheimer disease (AD), coronary artery disease (CAD), and ischemic stroke using Mendelian randomization. METHODS AND RESULTS: Using summary statistics from 4 recent, large genome-wide association studies of SA (n=523 366), AD (n=94 437), CAD (n=1 165 690), and stroke (n=1 308 460), we conducted bidirectional 2-sample Mendelian randomization analyses. Our primary analytic method was fixed-effects inverse variance-weighted (IVW) Mendelian randomization; diagnostics tests and sensitivity analyses were conducted to verify the robustness of the results. We identified a significant causal effect of SA on the risk of CAD (odds ratio [ORIVW]=1.35 per log-odds increase in SA liability [95% CI=1.25-1.47]) and stroke (ORIVW=1.13 [95% CI=1.01-1.25]). These associations were somewhat attenuated after excluding single-nucleotide polymorphisms associated with body mass index (ORIVW=1.26 [95% CI=1.15-1.39] for CAD risk; ORIVW=1.08 [95% CI=0.96-1.22] for stroke risk). SA was not causally associated with a higher risk of AD (ORIVW=1.14 [95% CI=0.91-1.43]). We did not find causal effects of AD, CAD, or stroke on risk of SA. CONCLUSIONS: These results suggest that SA increased the risk of CAD, and the identified causal association with stroke risk may be confounded by body mass index. Moreover, no causal effect of SA on AD risk was found. Future studies are warranted to investigate cardiovascular pathways between sleep disorders, including SA, and dementia.

Asunto(s)

RESUMEN

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records. However, in order to mitigate the labor-intensive nature of this process, automated approaches have been developed. These automated methods aim to streamline and facilitate sleep stage classification. This study aims to classify sleep stages in a dataset comprising subjects with insomnia, PLM, and sleep apnea. The dataset consists of PSG recordings from the multi-ethnic study of atherosclerosis (MESA) cohort of the national sleep research resource (NSRR), including 2056 subjects. Among these subjects, 130 have insomnia, 39 suffer from PLM, 156 have sleep apnea, and the remaining 1731 are classified as good sleepers. This study proposes an automated computerized technique to classify sleep stages, developing a machine-learning model with explainable artificial intelligence (XAI) capabilities using wavelet-based Hjorth parameters. An optimal biorthogonal wavelet filter bank (BOWFB) has been employed to extract subbands (SBs) from 30 seconds of electroencephalogram (EEG) epochs. Three EEG channels, namely: Fz_Cz, Cz_Oz, and C4_M1, are employed to yield an optimum outcome. The Hjorth parameters extracted from SBs were then fed to different machine learning algorithms. To gain an understanding of the model, in this study, we used SHAP (Shapley Additive explanations) method. For subjects suffering from the aforementioned diseases, the model utilized features derived from all channels and employed an ensembled bagged trees (EnBT) classifier. The highest accuracy of 86.8%, 87.3%, 85.0%, 84.5%, and 83.8% is obtained for the insomniac, PLM, apniac, good sleepers and complete datasets, respectively. Using these techniques and datasets, the study aims to enhance sleep stage classification accuracy and improve understanding of sleep disorders such as insomnia, PLM, and sleep apnea.

Asunto(s)

RESUMEN

BACKGROUND: Determining the self-efficacy perceptions of obstructive sleep apnea (OSA) patients has a key role in health care practices. With further evaluation, the Self-Efficacy Measure for Sleep Apnea (SEMSA) could serve as a useful scale to develop specific interventions to increase self-efficacy in patients with OSA during the acceptance and maintenance of continuous positive airway pressure (CPAP) therapy. OBJECTIVE: The aim of this study is to translate the SEMSA into Turkish and to evaluate the psychometric properties of the translation. METHODS: This cross-sectional study was carried out with a sample of patients recently diagnosed with CPAP-naïve OSA. Linguistic and content validity of the scale were evaluated, while exploratory factor analysis and 2-level confirmatory factor analysis were used for validity. Internal consistency and test-retest methods were used in reliability analyses. RESULTS: The mean (SD) age of the patients with OSA was 51.36 (11.29), and 68% were male. The item factor loads obtained as a result of the confirmatory factor analysis ranged from 0.44 to 0.94, confirming the three-factor structure of the instrument. The Cronbach's α coefficient of the scale was found to be 0.90. Measurements made within the scope of test-retest analysis were found to be related and consistent results were obtained in the intervening time (P < .01). CONCLUSIONS: In this study, the Turkish version of SEMSA was found to be a valid and reliable tool and it could be used to evaluate the adherence-related cognition in Turkish patients with OSA on CPAP therapy.

Asunto(s)

RESUMEN

OBJECTIVE: This study aimed to investigate the potential causal association between Sleep Apnea Syndrome (SAS) and Depression, focusing on the roles of gut microbiota, serum metabolites, and inflammatory factors in these conditions. METHODS: Mendelian Randomization (MR) analysis was performed using data from genome-wide association studies to assess 211 types of gut microbiota, 1400 serum metabolites, and 91 inflammatory factors as potential contributing factors. Causal inference was conducted using the Inverse Variance Weighted (IVW) method, with additional robustness checks through Cochran's Q test, MR-Egger regression intercept test, MR-PRESSO global test, and leave-one-out analysis. RESULTS: The MR analysis indicated a positive correlation between the risk of SAS and Depression (OR = 1.12, 95 % CI: 1.05-1.19, P < 0.001), with a reciprocal analysis showing a similar positive correlation between Depression and the risk of SAS (OR = 1.19, 95 % CI: 1.07-1.31, P = 0.001). Additionally, causal associations were identified between 15 types of gut microbiota, 36 serum metabolites, and 2 inflammatory factors with SAS, and between 11 types of gut microbiota, 23 serum metabolites, and 3 inflammatory factors with Depression (IVW, all P < 0.05). The robustness of these findings was confirmed through the MR-Egger regression intercept test and MR-PRESSO global test. CONCLUSION: This study provides epidemiological evidence of a bidirectional causal association between SAS and Depression, emphasizing the potential roles of gut microbiota, serum metabolites, and inflammatory factors in the pathogenesis of these disorders. These findings may inform the development of new therapeutic strategies.

Asunto(s)

RESUMEN

We performed a matched-pair analysis of the content of GDF11 and GDF15 proteins in the plasma of patients (56 middle-aged men) with obstructive sleep apnea syndrome (OSAS) and healthy volunteers (27 men with no complaints of sleep disorders). The groups were comparable in terms of age and presence of chronic diseases. No statistically significant differences in GDF11 content in the studied groups were revealed, while the content of GDF15 in the OSAS group was 1.3 times higher. These results require further research from the viewpoint of geriatric somnology and molecular biology.

Asunto(s)

RESUMEN

Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.

Asunto(s)

RESUMEN

(1) Background: Sleep-disordered breathing and asthma are often interrelated. Children and adults with asthma are more susceptible to sleep apnea. Inhaled corticosteroids effectively reduce inflammation and prevent structural changes in the airways. Objective: to explore the existing literature to determine whether inhaled corticosteroids play a role in sleep-disordered breathing in patients with asthma. (2) Methods: We conducted a thorough search of the PubMed, Scopus, and Web of Science databases for English-language articles published up to 12 May 2024. We utilized the ROBINS-E tool to assess the risk of bias. (4) Conclusions: 136 articles were discerned upon conducting the literature search. A total of 13 articles underwent exhaustive full-text scrutiny, resulting in 6 being considered non-relevant. The remaining seven articles, assessed for eligibility, were incorporated into the final analysis. Five studies were identified in adults and two in children. In adult patients, inhaled corticosteroids, especially at high doses, appear to increase the risk of sleep apnea in a dose-dependent manner. Moreover, the properties of inhaled corticosteroids, such as particle size, may impact the risk of developing sleep apnea. In children, the severity of asthma is a key factor affecting the prevalence of sleep apnea, whereas inhaled corticosteroids appear to be a less significant risk factor compared to adults. All of the studies reviewed were classified as having a high risk of bias or some concerns regarding bias. Each study revealed at least one type of bias that raised notable concerns. This research highlights a complex interaction between the use of inhaled corticosteroids, the severity of asthma, and the onset of sleep apnea. Additional research is necessary to investigate these relationships further.

Asunto(s)

RESUMEN

OBJECTIVE: To determine if perioperative administration of dexmedetomidine affects postoperative fluid intake in tonsillectomy patients. METHODS: A retrospective chart review was performed at University Medical Center, Texas Tech Health Science Center, Lubbock, Texas. The study identified 534 patients within the last five years who met the criteria. Common indications for the surgeries included recurrent tonsillitis, obstructive sleep apnea, and sleep disordered breathing. Patients with concurrent peritonsillar abscess drainage, microlaryngoscopy, bronchoscopy, supraglottoplasty, and other procedures that may impact fluid intake were excluded. The relationship between dexmedetomidine and fluid intake was evaluated using bivariate analysis as well as multivariable regression to account for possible confounders such as age, concurrent medication, surgery type, and method of surgery using STATA statistical software, version 17.0 (StataCorp LLC, College Station, TX). RESULTS: Administration of dexmedetomidine did not significantly impact the amount of fluid intake, fluid intake per kilogram per hour, or average postoperative pain levels in patients who underwent tonsillectomy or adenotonsillectomy in the bivariate analysis (p = 0.217, 0.489, 0.512 respectively) and multiple regression model (p = 0.156, 0.802, 0.795) CONCLUSION: Dexmedetomidine does not negatively influence postoperative fluid intake levels in patients and should continue to be utilized in appropriately selected patients experiencing anxiety or agitation prior to surgery.

Asunto(s)

RESUMEN

INTRODUCTION: Despite disease modifying treatments (DMT), assisted ventilation is commonly required in children with Spinal Muscular Atrophy (SMA). Guidelines suggest screening with oximetry and transcutaneous carbon dioxide (TcCO2) for sleep disordered breathing (SDB). AIM: To determine the utility of pulse oximetry and TcCO2 as a screen for SDB and the need for Non-Invasive Ventilation (NIV) in children with SMA type 1-3. METHODS: A prospective cohort study was conducted in Queensland, Australia. Full diagnostic PSG was completed in DMT naïve children with SMA. Pulse oximetry and TcCO2 were extracted from PSG. Apnoea-hypopnoea indices (AHI) criteria were applied to PSG results to define the need for NIV. Abnormal was defined as: ≤3 months of age [mo] AHI≥10 events/hour; >3mo AHI ≥5 events/hour. Receiver operating characteristic curves were calculated for abnormal PSG and pulse oximetry/TcCO2 variables, and diagnostic statistics were calculated. RESULTS: Forty-seven untreated children with SMA were recruited (type 1 n = 13; 2 n = 21; 3 n = 13) ranging from 0.2 to 18.8 years old (median 4.9 years). Oxygen desaturation index ≥4 % (ODI4) ≥20events/hour had sensitivity 82.6 % (95 % CI 61.2-95.0) and specificity of 58.3 % (95 % CI 36.6-77.9). TcCO2 alone and combinations of oximetry/TcCO2 had low diagnostic ability. The same methodology was applied to 36 children who were treated (type 1 n = 7; type 2 n = 17; type n = 12) and oximetry±TcCO2 had low diagnostic ability. CONCLUSION: ODI4 ≥20events/hour can predict the need for NIV in untreated children with SMA. TcCO2 monitoring does not improve the PPV. If normal however, children may still require a diagnostic PSG. Neither oximetry nor TcCO2 monitoring were useful screening tests in the children treated with DMT.

Asunto(s)

RESUMEN

BACKGROUND: Sleep is a fundamental and complex physiological process whose duration decreases and characteristics change with age. Around 50 % of children will experience sleep disturbances at some point in their early life. Sleep disturbances can result in a number of deleterious consequences, including alterations in the levels of cellular senescence (CS) markers. CS is a complex process essential for homeostasis characterized by the irreversible loss of cell proliferation capacity; however, the accumulation of senescent cells can lead to age-related diseases. OBJECTIVE: In this review, our objective was to gather information about the relationship between sleep duration, sleep-disordered breathing (SDB) and cellular senescence markers, namely: oxidative stress, inflammation, insulin-like growth factor 1 (IGF-1), and growth hormone (GH) in newborns, children, and teenagers. METHODS: To achieve this, we searched six databases: MEDLINE, Scopus, LILACS, Web of Science, Embase, and SciELO, and identified 20 articles that met our inclusion criteria. RESULTS: Our results show that better sleep quality and duration and, both the surgical and non-surgical treatment of sleep disorders are associated with a reduction in oxidative stress, inflammation, and telomeric attrition levels. Furthermore, our results also show that surgical treatment for SDB significantly reduced the levels of cellular senescence markers. Further studies need to be conducted in this area, particularly longitudinal studies, for a greater understanding of the mechanisms involved in the relationship between sleep and senescence. CONCLUSION: Better sleep quality and duration were associated with less oxidative stress, inflammation, and telomeric attrition and a higher level of IGF-1 in children and teenagers.

Asunto(s)

RESUMEN

BACKGROUND: Sleep Disorder Breathing (SDB) in pregnant patients ranges from 3 to 27% and varies depending on gestational age and method used to diagnose. SDB increases the risk of advanced pregnancy complications such as gestational diabetes mellitus, pregnancy-induced hypertension, and preeclampsia. Screening and diagnosis of SDB during pregnancy remains a challenge, with existing screening tools underperforming during pregnancy. This study aimed to validate a previously developed model for predicting SDB during late pregnancy and compare the predictive value of bedpartner responses. METHODS: Ninety-six women in the third trimester of pregnancy underwent polysomnography and completed the Berlin Questionnaire (BQ), with 81 bedpartners completing the BQ about their pregnant partner. A subset of BQ items (snoring volume and tiredness upon awakening) along with BMI > 32 kg/m2 was utilised to calculate the Wilson Optimized Model (WOM), which demonstrated strong predictive properties in development. RESULTS: SDB (RDI/hr ≥ 5) was detected in 43.8% of women. BQ identified 72% of pregnant mothers as high risk for SDB (Sensitivity = 83%, Specificity = 37%), compared to 29% of mothers identified by the WOM (Sensitivity = 45%, Specificity = 83%). At RDI of ≥ 15, the WOM correctly classified more women according to SDB risk than the BQ (76.0% vs. 41.7% cases correct, X2(1) = 23.42, p < .001), with no difference at RDI ≥ 5. Bedpartners were more likely to report high risk for SDB on the WOM than pregnant women themselves (38.3% vs. 28.4%), however predictive ability was not improved by bedpartner input (RDI ≥ 5 bedpartner AUC = 0.69 v mother AUC = 0.73). CONCLUSION: BQ largely overestimates the prevalence of SDB in pregnancy compared to the WOM which underestimates. Utilising bedpartner responses didn't improve screening for SDB in late pregnancy. More work is needed to develop a pregnancy-specific tool for quick and accurate screening for SDB.

Asunto(s)

RESUMEN

Sleep is a physiological state that is essential for maintaining physical and mental health. Sleep disorders and sleep deprivation therefore have many adverse effects, including an increased risk of metabolic diseases and a decline in cognitive function that may be implicated in the long-term development of neurodegenerative diseases. There is increasing evidence that microglia, the resident immune cells of the central nervous system (CNS), are involved in regulating the sleep-wake cycle and the CNS response to sleep alteration and deprivation. In this chapter, we will discuss the involvement of microglia in various sleep disorders, including sleep-disordered breathing, insomnia, narcolepsy, myalgic encephalomyelitis/chronic fatigue syndrome, and idiopathic rapid-eye-movement sleep behavior disorder. We will also explore the impact of acute and chronic sleep deprivation on microglial functions. Moreover, we will look into the potential involvement of microglia in sleep disorders as a comorbidity to Alzheimer's disease and Parkinson's disease.

Asunto(s)

RESUMEN

Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China's sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment via end-tidal CO 2 and O 2 concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.

Asunto(s)

RESUMEN

BACKGROUND: Sleep apnea is a widespread and yet still underdiagnosed condition. Various studies from the past have provided evidence that there is a link between sleep apnea and various cardiovascular diseases, including arrhythmias. OBJECTIVE: The aim of this article is to provide an overview of the current study situation and to point out possible consequences relevant to everyday life. MATERIAL AND METHODS: A systematic search was carried out in various databases using the keywords sleep apnea (OSAS/SA) and arrhythmias/dysrhythmias. RESULTS: There are several pathophysiological links between sleep-related breathing disorders and cardiac arrhythmias, the most important of which appear to be intrathoracic pressure, increased adrenergic tone as well as recurrent hypoxia and hypercapnia. This results in an increased occurrence of clinically relevant arrhythmias, such as atrial fibrillation, symptomatic bradycardia, high-grade atrioventricular (AV) blocks as well as ventricular arrhythmias in patients with untreated sleep apnea. These pathologies also appear to be positively influenced by the treatment of sleep apnea. CONCLUSION: A close correlation between sleep apnea and cardiac arrhythmias is undisputed. Large randomized studies in this respect are so far rare but it is undisputed that a thorough search should be carried out for sleep apnea and consistently treated in patients with a history of cardiac disease as this can have a relevant influence on the treatment and ultimately the prognosis of the patient.

Asunto(s)

RESUMEN

The choice of interface used to deliver noninvasive ventilation (NIV) is a critical element in successfully and safely establishing home NIV in people with sleep hypoventilation syndromes. Both patient-related and equipment-related factors need to be considered when selecting an interface. Recognizing specific issues that can occur with a particular style of mask is important when troubleshooting NIV problems and attempting to minimize side effects. Access to a range of mask styles and designs to use on a rotational basis is especially important for patients using NIV on a more continuous basis, those at risk of developing pressure areas, and children.

Asunto(s)

RESUMEN

BACKGROUND: Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods. METHOD: This study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal's dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model's efficacy in identifying SLA occurrences. RESULTS: The proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %. CONCLUSIONS: These results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.

Asunto(s)