Breathing Events Research Articles

Left atrial appendage aneurysm: a descriptive systematic review of 177 cases.

The left atrial appendage (LAA) is a distinct structure with unique developmental and structural characteristics. The LAA is involved in the formation of intra-atrial thrombi, particularly in patients with conditions such as atrial fibrillation and mitral valve disease. Left atrial appendage aneurysms (LAAA) are rare abnormal dilations of the LAA that may cause hazardous complications. However, there are limited data on the demographic features, clinical characteristics, management modalities, and prognosis of LAAA patients. This study aimed to conduct a systematic review of the reported cases of LAAA to explore the baseline characteristics, presentation, preferred diagnostic modalities, and optimal management of LAAA. A systematic review was conducted following the PRISMA guidelines. We performed a literature search using MEDLINE/PubMed and Google Scholar. Eligible articles published between January 1940 and November 2022 were included. The eligibility criteria included case reports and case series of LAAA in English language articles. The data extracted included information on the authors, publication year, patient characteristics, signs/symptoms, diagnostic procedures, treatments, and outcomes. We identified 177 patients with LAAA in our study. There was a slight female predominance (50.9%), and the mean age was 29.7 years. Palpitations were the most common symptom reported, followed by shortness of breath and thromboembolic events. Transthoracic and transesophageal echocardiograms were the most common modalities for investigating and diagnosing LAAA, and the mean size of the aneurysm was 7.8 (5.7-9.6) × 5.9 (5.0-6.2) cm. Surgical resection is the treatment of choice for most patients with excellent prognoses. Older age and the presence of arrhythmia were significantly associated with thrombus formation and embolic events. Left atrial appendage aneurysm is a rare but potentially life-threatening heart pathology that can lead to arrhythmias and thromboembolic events. Surgical resection appears to be the primary treatment option in the current literature, and most patients show improvement or are asymptomatic after treatment. Additionally, alternative approaches, such as transcatheter closure of LAAA, ablation, and medical treatments, have been reported as viable alternatives to surgical intervention.

Porous PDMS-ZnO Wearable Gas Sensor for Acetone Biomarker Detection and Breath Analysis.

In response to the growing demand for global health monitoring, we report a nonintrusive health detection method using a compact, conformal wearable ultraviolet (UV)-assisted gas-sensing system based on an intrinsically flexible porous polydimethylsiloxane (PDMS)-zinc oxide (ZnO) composite layer (PPZL) for the breath acetone (BrAce) detection and breath event analysis. The enhanced acetone response is attributed to the synergistic effect of UV irradiation and the high surface area of the porous structure, which also improves the mechanical robustness. The UV-assisted wearable sensor reliably detects acetone concentrations ranging from 1 to 100 ppm at room temperature under 4.05 mW/cm2 UV intensity, even under mechanical strains such as a bending radius of 5 mm and 60% tensile strain. It accurately analyzes different breathing patterns (12-20 breaths per minute) and BrAce concentrations, maintaining a stable performance over 20 days with less than 5% signal degradation. The sensor exhibits response and recovery times of average 110-150 and 130-180 s, respectively, and maintains a consistent 3 ppm BrAce response under varying humidity levels up to 70% relative humidity, ensuring accurate detection of BrAce concentrations during real-world breath tests. Additionally, the sensor targets only specific gases, and the sensor's selectivity is not a key concern. This flexible acetone gas sensor offers a portable solution for health management and a fabrication method for designing flexible metal oxide materials.

Physiologic and anatomic determinants of hyoid motion during drug-induced sleep endoscopy

PurposeTo examine factors accounting for differences in hyoid motion during obstructive breathing events amongst obstructive sleep apnea (OSA) patients.MethodsThis was a prospective cohort study from June 2022 to October 2022. Patients with OSA undergoing evaluation for PAP alternative therapies with drug-induced sleep endoscopy with positive airway pressure titration (DISE-PAP). All patients underwent DISE-PAP and concurrent hyoid-focused ultrasound. DISE-PAP enabled measurement of airway physiology (flow, respiratory effort) and airway collapsibility (pharyngeal opening pressure, PhOP). Hyoid-ultrasound enabled hyoid bone movement during obstructive breathing. Respiratory effort was measured using a retro-epiglottic pressure-sensitive catheter. Hyoid position was measured using a standardized, awake, CT protocol. Regression analyses adjusted for age, race, sex, and BMI were performed to associate indices of respiratory effort and CT data with hyoid motion.ResultsOn average, the 26 patients in this cohort were older (63.9 ± 10.5 years), male (69%), overweight (29.6 ± 3.99 kg/m2), and with moderate-to-severe OSA (26.8 ± 10.4 events/hour). Greater respiratory effort was associated with increased hyoid motion (β [95% CI] = 0.034 [0.016,0.052], standardized β = 0.261,p = 0.0003). Higher hyoid position was associated with greater hyoid displacement (β [95% CI] = -0.20 [-0.38,-0.01], Standardized β = -0.57, p = 0.036).ConclusionOur data demonstrate that greater respiratory effort, higher hyoid position, and higher airway collapsibility, but not airflow, are associated with greater hyoid motion during obstructive breathing in DISE. These findings suggest that downward hyoid movement represents a compensatory response to upper airway obstruction. Further studies should investigate the vectors of hyoid motion to better understand its role in sleep-related airway collapse.

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks.

wearable sensor technology has progressed significantly in the last decade, but its clinical usability for the assessment of obstructive sleep apnea (OSA) is limited by the lack of large and representative datasets simultaneously acquired with polysomnography (PSG). The objective of this study was to explore the use of cardiorespiratory signals common in standard PSGs which can be easily measured with wearable sensors, to estimate the severity of OSA. an artificial neural network was developed for detecting sleep disordered breathing events using electrocardiogram (ECG) and respiratory effort. The network was combined with a previously developed cardiorespiratory sleep staging algorithm and evaluated in terms of sleep staging classification performance, apnea-hypopnea index (AHI) estimation, and OSA severity estimation against PSG on a cohort of 653 participants with a wide range of OSA severity. four-class sleep staging achieved a κ of 0.69 versus PSG, distinguishing wake, combined N1-N2, N3 and REM. AHI estimation achieved an intraclass correlation coefficient of 0.91, and high diagnostic performance for different OSA severity thresholds. this study highlights the potential of using cardiorespiratory signals to estimate OSA severity, even without the need for airflow or oxygen saturation (SpO2), traditionally used for assessing OSA. while further research is required to translate these findings to practical and unobtrusive sensors, this study demonstrates how existing, large datasets can serve as a foundation for wearable systems for OSA monitoring. Ultimately, this approach could enable long-term assessment of sleep disordered breathing, facilitating new avenues for clinical research in this field.

Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review

Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing the PSALSAR framework, incorporating the PICOC method and PRISMA statement for comprehensive research, 22 publications were scrutinized from the Scopus, Web of Science, and PubMed databases. A diverse range of sensor fusion methods, data signal analysis techniques, and classifier performances were investigated. Notably, Madgwick’s algorithm and the Principal Component Analysis showed superior performance in tracking respiratory movements. Classifiers like Long Short-Term Memory Recurrent Neural Networks exhibited high accuracy in detecting breathing events. Motion artifacts, limited sample sizes, and physiological variability posed challenges, highlighting the need for further research. Optimal sensor configurations were explored, suggesting improvements with multiple sensors, especially in different body postures. In conclusion, this systematic literature review elucidates methods, challenges, and potential future developments in using inertial sensors for assessing adult human breathing patterns. Overcoming the challenges related to sensor placement, motion artifacts, and algorithm development is essential for progress. Future research should focus on extending sensor applications to clinical settings and diverse populations, enhancing respiratory health management.

Quantitative validation of the suprasternal pressure signal to assess respiratory effort during sleep

Objective. Intra-esophageal pressure (Pes) measurement is the recommended gold standard to quantify respiratory effort during sleep, but used to limited extent in clinical practice due to multiple practical drawbacks. Respiratory inductance plethysmography belts (RIP) in conjunction with oronasal airflow are the accepted substitute in polysomnographic systems (PSG) thanks to a better usability, although they are partial views on tidal volume and flow rather than true respiratory effort and are often used without calibration. In their place, the pressure variations measured non-invasively at the suprasternal notch (SSP) may provide a better measure of effort. However, this type of sensor has been validated only for respiratory events in the context of obstructive sleep apnea syndrome (OSA). We aim to provide an extensive verification of the suprasternal pressure signal against RIP belts and Pes, covering both normal breathing and respiratory events. Approach. We simultaneously acquired suprasternal (207) and esophageal pressure (20) signals along with RIP belts during a clinical PSG of 207 participants. In each signal, we detected breaths with a custom algorithm, and evaluated the SSP in terms of detection quality, breathing rate estimation, and similarity of breathing patterns against RIP and Pes. Additionally, we examined how the SSP signal may diverge from RIP and Pes in presence of respiratory events scored by a sleep technician. Main results. The SSP signal proved to be a reliable substitute for both esophageal pressure (Pes) and respiratory inductance plethysmography (RIP) in terms of breath detection, with sensitivity and positive predictive value exceeding 75%, and low error in breathing rate estimation. The SSP was also consistent with Pes (correlation of 0.72, similarity 80.8%) in patterns of increasing pressure amplitude that are common in OSA. Significance. This work provides a quantitative analysis of suprasternal pressure sensors for respiratory effort measurements.

Repeated Daily Exposure to Mild Intermittent Hypoxia (dMIH) Enhances the Critical Closing Pressure for 4 Weeks in Adults with OSA

Background and Objective: dMIH leads to improved upper airway patency, although it is unknown if this improvement is sustained for a prolonged time period. Enhancement of airway patency could lead to improved breathing stability during sleep in individuals with obstructive sleep apnea (OSA). However, exposure to MIH on a single occasion, immediately prior to the documentation of breathing events, induced breathing instability. This may have occurred because exposure to MIH leads to an increase in the ventilatory response to hypoxia, which induces breathing instability, despite improvements in airway patency. If so, enhanced upper airway patency coupled with mitigation of the ventilatory response to hypoxia could be key to improved breathing stability. In the present investigation, we explored if dMIH and the timing of its administration, coupled with abolishment of natural occurring nocturnal hypoxia, initiated the key responses to improve breathing stability for up to 8 weeks. Methods: 19 participants with OSA were randomly allocated to two groups. One group was exposed to dMIH, while the other group was exposed to sham dMIH. 5 participants in each group were also assigned to nightly use of continuous positive airway pressure (CPAP) for the duration of the study. Participants in the dMIH group were exposed to twelve 2-minute hypoxic episodes (PETO2 ~ 50 mmHg) separated by 2 - minute normoxic intervals (with PETCO2 sustained 2 mmHg above baseline) for 15 days. Participants in the sham group were exposed to compressed room air. Two sleep studies were completed before exposure to dMIH or Sham dMIH, as well as, 4 days, 4 weeks, and 8 weeks after exposure. During the initial study, standard sleep measures of sleep architecture and breathing were obtained (NB participants were not treated with CPAP during these studies). Throughout the second sleep study, measures of the critical closing pressure (PCRIT) were obtained. Results: Anthropometric measures were similar between the two groups [n = 9 vs. n = 10; age - 45.22 ± 8.66 vs. age - 47.1 ± 9.48 years; body mass index - 31.59 ± 5.49 kg/m2 vs. 34.79 ± 4.57 kg/m2; apnea hypopnea index (AHI) - 58.39 ± 17.57 vs. 70.18 ± 22.42 events/hr.; P > 0.18 for all comparisons]. Airway patency was enhanced (i.e., a less positive PCRIT) 4 days (3.01 ± 4.040 vs. 5.79 ± 2.76 cmH2O, P = 0.043) and 4 weeks (2.96 ± 3.38 vs. 5.79 ± 2.76 cmH2O, P = 0.038) after exposure to dMIH compared to baseline. This improvement was not evident in the Sham dMIH group (P > 0.168 for all comparisons). Compared to baseline, there was some evidence that the AHI during non-rapid eye movement sleep was improved in the sub-group of participants that were treated nightly with CPAP throughout the dMIH protocol (57.13 ± 5.06 vs. 40.08 ± 12.54 [4 days] P = 0.07 vs. 35.73 ± 8.19 [4 weeks] events/hr.; P = 0.036). However, this improvement was not evident in the dMIH group as a whole. Conclusion: dMIH led to improvements in upper airway patency that were sustained for 4 weeks. Despite the improved airway patency, no significant improvements in breathing stability were evident in the dMIH group as a whole. However, in a sub-group of participants exposed to dMIH coupled to nightly CPAP some improvements were evident. Whether the reduced AHI in this group is a consequence of improvements in airway patency combined with mitigation of the ventilatory response to hypoxia, via the elimination of nightly hypoxemia, requires further investigation. I01CX000125, IK6CX002287, R01HL142757, R56HL142757. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Fentanyl activates opposing opioid and non-opioid receptor systems that control breathing.

Fentanyl elicits profound disturbances in ventilatory control processes in humans and experimental animals. The traditional viewpoint with respect to fentanyl-induced respiratory depression is that once the effects on the frequency of breathing (Freq), tidal volume (TV), and minute ventilation (MV = Freq × TV) are resolved, then depression of breathing is no longer a concern. The results of the present study challenge this concept with findings, as they reveal that while the apparent inhibitory effects of fentanyl (75μg/kg, IV) on Freq, TV, and MV in adult male rats were fully resolved within 15 min, many other fentanyl-induced responses were in full effect, including opposing effects on respiratory timing parameters. For example, although the effects on Freq were resolved at 15 min, inspiratory duration (Ti) and end inspiratory pause (EIP) were elevated, whereas expiratory duration (Te) and end expiratory pause (EEP) were diminished. Since the effects of fentanyl on TV had subsided fully at 15 min, it would be expected that the administration of an opioid receptor (OR) antagonist would have minimal effects if the effects of fentanyl on this and other parameters had resolved. We now report that the intravenous injection of a 1.0mg/kg dose of the peripherally restricted OR antagonist, methyl-naloxone (naloxone methiodide, NLXmi), did not elicit arousal but elicited some relatively minor changes in Freq, TV, MV, Te, and EEP but pronounced changes in Ti and EIP. In contrast, the injection of a 2.5mg/kg dose of NLXmi elicited pronounced arousal and dramatic changes in many variables, including Freq, TV, and MV, which were not associated with increases in non-apneic breathing events such as apneas. The two compelling conclusions from this study are as follows: 1) the blockade of central ORs produced by the 2.5mg/kg dose of NLXmi elicits pronounced increases in Freq, TV, and MV in rats in which the effects of fentanyl had apparently resolved, and 2) it is apparent that fentanyl had induced the activation of two systems with counter-balancing effects on Freq and TV: one being an opioid receptor inhibitory system and the other being a non-OR excitatory system.

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

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. 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. 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. 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. 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.

Adenotonsillectomy for Snoring and Mild Sleep Apnea in Children

The utility of adenotonsillectomy in children who have habitual snoring without frequent obstructive breathing events (mild sleep-disordered breathing [SDB]) is unknown. To evaluate early adenotonsillectomy compared with watchful waiting and supportive care (watchful waiting) on neurodevelopmental, behavioral, health, and polysomnographic outcomes in children with mild SDB. Randomized clinical trial enrolling 459 children aged 3 to 12.9 years with snoring and an obstructive apnea-hypopnea index (AHI) less than 3 enrolled at 7 US academic sleep centers from June 29, 2016, to February 1, 2021, and followed up for 12 months. Participants were randomized 1:1 to either early adenotonsillectomy (n = 231) or watchful waiting (n = 228). The 2 primary outcomes were changes from baseline to 12 months for caregiver-reported Behavior Rating Inventory of Executive Function (BRIEF) Global Executive Composite (GEC) T score, a measure of executive function; and a computerized test of attention, the Go/No-go (GNG) test d-prime signal detection score, reflecting the probability of response to target vs nontarget stimuli. Twenty-two secondary outcomes included 12-month changes in neurodevelopmental, behavioral, quality of life, sleep, and health outcomes. Of the 458 participants in the analyzed sample (231 adenotonsillectomy and 237 watchful waiting; mean age, 6.1 years; 230 female [50%]; 123 Black/African American [26.9%]; 75 Hispanic [16.3%]; median AHI, 0.5 [IQR, 0.2-1.1]), 394 children (86%) completed 12-month follow-up visits. There were no statistically significant differences in change from baseline between the 2 groups in executive function (BRIEF GEC T-scores: -3.1 for adenotonsillectomy vs -1.9 for watchful waiting; difference, -0.96 [95% CI, -2.66 to 0.74]) or attention (GNG d-prime scores: 0.2 for adenotonsillectomy vs 0.1 for watchful waiting; difference, 0.05 [95% CI, -0.18 to 0.27]) at 12 months. Behavioral problems, sleepiness, symptoms, and quality of life each improved more with adenotonsillectomy than with watchful waiting. Adenotonsillectomy was associated with a greater 12-month decline in systolic and diastolic blood pressure percentile levels (difference in changes, -9.02 [97% CI, -15.49 to -2.54] and -6.52 [97% CI, -11.59 to -1.45], respectively) and less progression of the AHI to greater than 3 events/h (1.3% of children in the adenotonsillectomy group compared with 13.2% in the watchful waiting group; difference, -11.2% [97% CI, -17.5% to -4.9%]). Six children (2.7%) experienced a serious adverse event associated with adenotonsillectomy. In children with mild SDB, adenotonsillectomy, compared with watchful waiting, did not significantly improve executive function or attention at 12 months. However, children with adenotonsillectomy had improved secondary outcomes, including behavior, symptoms, and quality of life and decreased blood pressure, at 12-month follow-up. ClinicalTrials.gov Identifier: NCT02562040.

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.

Obstructive sleep apnea during rapid eye movement sleep and cognitive performance in adults

Study objectivesObstructive sleep apnea (OSA) during rapid eye movement (REM) sleep is often characterized with more frequent and lengthy breathing events and greater oxygen desaturation than during other sleep stages. Current evidence suggests an association between OSA and cognitive decline, however whether OSA during REM sleep plays a vital role in this link is understudied. MethodsA cross-sectional sample of 728 men and women (aged 59.1 ± 11.3 years) underwent a full night polysomnography for determining apnea-hypopnea index (AHI) and sleep stages. Trail Making Test (TMT) part A and B were conducted during the following day for assessing participants' cognitive function. Linear regression analyses were performed to test the possible association between AHI and AHI during REM sleep with TMT-A and B results. Similar analyses were carried out in a subsample involving participants aged ≥60 years with ≥30 min of REM sleep (n = 356). ResultsDespite a slight difference in TMT-B between participants with and without OSA (AHI ≥5 vs AHI <5, β-coefficient: 4.83, 95 % CI: [-9.44, −0.22], P = 0.040), no other association between AHI or REM-AHI and TMT results were found in the full sample. In older participants (aged ≥60 years), a REM-AHI ≥5 events/hour was associated with longer time taken to finish TMT-A (vs REM-AHI <5 events/hour, 3.93, [0.96, 6.90], P = 0.010). There was no association between REM-AHI and time taken to finish TMT-B in older participants. ConclusionsThe results indicate that OSA during REM sleep may be of particular concern for attention-related cognitive function in older adults.

The incidence of potentially serious complications during non-obstetric anaesthetic practice in the United Kingdom: an analysis from the 7th National Audit Project (NAP7) activity survey.

Complications and critical incidents arising during anaesthesia due to patient, surgical or anaesthetic factors, may cause harm themselves or progress to more severe events, including cardiac arrest or death. As part of the 7th National Audit Project of the Royal College of Anaesthetists, we studied a prospective national cohort of unselected patients. Anaesthetists recorded anonymous details of all cases undertaken over 4 days at their site through an online survey. Of 416 hospital sites invited to participate, 352 (85%) completed the survey. Among 24,172 cases, 1922 discrete potentially serious complications were reported during 1337 (6%) cases. Obstetric cases had a high reported major haemorrhage rate and were excluded from further analysis. Of 20,996 non-obstetric cases, 1705 complications were reported during 1150 (5%) cases. Circulatory events accounted for most complications (616, 36%), followed by airway (418, 25%), metabolic (264, 15%), breathing (259, 15%), and neurological (41, 2%) events. A single complication was reported in 851 (4%) cases, two complications in 166 (1%) cases and three or more complications in 133 (1%) cases. In non-obstetric elective surgery, all complications were 'uncommon' (10-100 per 10,000 cases). Emergency (urgent and immediate priority) surgery accounted for 3454 (16%) of non-obstetric cases but 714 (42%) of complications with severe hypotension, major haemorrhage, severe arrhythmias, septic shock, significant acidosis and electrolyte disturbances all being 'common' (100-1000 per 10,000 cases). Based on univariate analysis, complications were associated with: younger age; higher ASA physical status; male sex; increased frailty; urgency and extent of surgery; day of the week; and time of day. These data represent the rates of potentially serious complications during routine anaesthesia care and may be valuable for risk assessment and patient consent.

Abstract 18921: Obstructive Sleep Apnea in the Presence of Heart Failure With Preserved Ejection Fraction is Associated With Low Arousal Threshold and Increased Sleep Fragmentation

Introduction: Heart failure with preserved ejection fraction (HFpEF) is often accompanied by obstructive sleep apnea (OSA). The effects of HFpEF on OSA pathophysiology remain relatively unexplored. Pulmonary congestion and sympathetic nervous activity (SNA) may drive ventilatory control instability (high loop gain [LG]). Elevated SNA also theoretically predisposes sleep to easier interruption by breathing events of OSA, reflected by low arousal threshold (ArTH). Hypothesis: In comparison to patients with OSA alone, patients with both OSA and HFpEF have higher LG and lower ArTH. Methods: We reviewed records to include patients with 1) transthoracic echocardiography with EF&gt;40% and left ventricular diastolic dysfunction by decreased age-adjusted e’ and/or left atrial volume index &gt;28 mL/m 2 ; 2) OSA by apnea-hypopnea index (AHI3A) ≥15 events/hour; and 3) raw polysomnography (PSG) data available for advanced signal analysis. LG and ArTH were estimated by Phenotyping Using Polysomnography (PUP), a MATLAB algorithm that quantifies pathophysiologic mechanisms of OSA using PSG data. Values in HFpEF+OSA patients were compared to values from research volunteers with OSA alone. Results: The HFpEF+OSA group included 21 patients. The OSA alone group included 43 research volunteers. LG was lower for HFpEF+OSA (0.63±18) compared to OSA alone (0.69±18), but the difference was not statistically significant. ArTH was significantly lower for HFpEF+OSA (117.0±15.9 %eupnea) compared to OSA alone (152.8±54.8 %eupnea, p = &lt;0.001) and remained significant after adjustment for age, sex, and body mass index. ArTH was correlated with sleep efficiency (R = 0.43, p = &lt;0.001) and negatively correlated with wake after sleep onset (R = -0.35, p =0.005). Conclusions: HFpEF is associated with low arousal threshold, which may contribute to sleep fragmentation. Our data did not confirm the hypothesis of greater breathing instability in patients with HFpEF and OSA compared to OSA alone.

Sleep Apnea Prediction Using Deep Learning.

Obstructive sleep apnea (OSA) is a sleep disorder that causes partial or complete cessation of breathing during an individual's sleep. Various methods have been proposed to automatically detect OSA events, but little work has focused on predicting such events in advance, which is useful for the development of devices that regulate breathing during a patient's sleep. We propose four methods for sleep apnea prediction based on convolutional and long short-term memory neural networks (1D-CNN, ConvLSTM, 1D-CNN-LSTM and 2D-CNN-LSTM), which use raw data from three respiratory signals (nasal flow, abdominal and thoracic) sampled at 32Hz, without any human-engineered features. We predict OSA (apnea or hypopnea) and normal breathing events 30 seconds ahead using the prior 90 seconds' data. Our results on a dataset containing over 46,000 examples from 1,507 subjects show that all four models achieved promising accuracy ( 81%). The 1D-CNN-LSTM and 2D-CNN-LSTM were the best two performing models with accuracy, sensitivity and specificity over 83%, 81% and 85% respectively. These results show that OSA events can be accurately predicted in advance based on respiratory signals, opening up opportunities for the development of devices to preemptively regulate the airflow to sleepers to avoid these events. Furthermore, we demonstrate good prediction performance even when respiratory signals are downsampled by a factor of 32, to 1Hz, for which our proposed 1D-CNN-LSTM achieved 82.94% accuracy, 81.25% sensitivity and 84.63% specificity. This robustness to low sampling frequencies allows our algorithms to be implemented in devices with low storage capacity, making them suitable for at-home environments.

Maturation of cardioventilatory physiological trajectories in extremely preterm infants.

In extremely preterm infants, persistence of cardioventilatory events is associated with long-term morbidity. Therefore, the objective was to characterize physiologic growth curves of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants during the first few months of life. The Prematurity-Related Ventilatory Control study included 717 preterm infants <29 weeks gestation. Waveforms were downloaded from bedside monitors with a novel sharing analytics strategy utilized to run software locally, with summary data sent to the Data Coordinating Center for compilation. Apnea, periodic breathing, and intermittent hypoxemia events rose from day 3 of life then fell to near-resolution by 8-12 weeks of age. Apnea/intermittent hypoxemia were inversely correlated with gestational age, peaking at 3-4 weeks of age. Periodic breathing was positively correlated with gestational age peaking at 31-33 weeks postmenstrual age. Females had more periodic breathing but less intermittent hypoxemia/bradycardia. White infants had more apnea/periodic breathing/intermittent hypoxemia. Infants never receiving mechanical ventilation followed similar postnatal trajectories but with less apnea and intermittent hypoxemia, and more periodic breathing. Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. Physiologic curves of cardiorespiratory events in extremely preterm-born infants offer (1) objective measures to assess individual patient courses and (2) guides for research into control of ventilation, biomarkers and outcomes. Presented are updated maturational trajectories of apnea, periodic breathing, intermittent hypoxemia, and bradycardia in 717 infants born <29 weeks gestation from the multi-site NHLBI-funded Pre-Vent study. Cardioventilatory events peak during the first month of life but the actual postnatal trajectory is dependent on the type of event, race, sex and use of mechanical ventilation. Different time courses for apnea and periodic breathing suggest different maturational mechanisms.

Adult perioperative cardiac arrest: An overview of 684 cases reported to webAIRS.

There were 684 perioperative cardiac arrests reported to webAIRS between September 2009 and March 2022. The majority involved patients older than 60 years, classified as American Society of Anesthesiologists Physical Status 3 to 5, undergoing an emergency or major procedure. The most common precipitants included airway events, cardiovascular events, massive blood loss. medication issues, and sepsis. The highest mortality rate was 54% of the 46 cases in the miscellaneous category (this included 34 cases of severe sepsis, which had a mortality of 65%). This was followed by cardiovascular precipitants (n = 424) in which there were 147 deaths (35% mortality): these precipitants included blood loss (53%), embolism (61%) and myocardial infarction (70%). Airway and breathing events accounted for 25% and anaphylaxis 8%. A specialist anaesthetist attended the majority of these cardiac arrests. As webAIRS is a voluntary database, it is not possible to determine the incidence of perioperative cardiac arrest and only descriptive information on factors associated with cardiac arrest can be obtained. Nevertheless, the large number of reports includes a wide range of cases, precipitants, demographics and outcomes, providing ample opportunity to learn from these events. The data also provide rich scope for further research into further initiatives to prevent cardiac arrest in the perioperative period, and to improve outcomes, should a cardiac arrest occur.

Impact of sleep-disordered breathing on overnight changes in arterial stiffness in patients with acute heart failure.

Overnight increases in arterial stiffness associated with sleep-disordered breathing may adversely affect patients with acute heart failure. Thus, we investigated overnight changes in arterial stiffness and their association with sleep-disordered breathing in patients hospitalized for acute heart failure. Consecutive patients with acute heart failure were enrolled. All participants underwent overnight full polysomnography following the initial improvement of acute signs and symptoms of acute heart failure. The arterial stiffness parameter, cardio-ankle vascular index (CAVI), was assessed before and after polysomnography. Overall, 60 patients (86.7% men) were analyzed. CAVI significantly increased overnight (from 8.4 ± 1.6 at night to 9.1 ± 1.7 in the morning, P < 0.001) in addition to systolic and diastolic blood pressure (from 114.1 mmHg to 121.6 mmHg, P < 0.001; and from 70.1 mmHg to 78.2 mmHg, P < 0.001, respectively). Overnight increase in CAVI (ΔCAVI ≥ 0) was observed in 42 patients (70%). The ΔCAVI ≥ 0 group was likely to have moderate-to-severe sleep-disordered breathing (i.e., apnea-hypopnea index ≥15, 55.6% vs 80.9%, P = 0.047) and greater obstructive respiratory events (29.4% vs 58.5%, P = 0.041). In multivariable analysis, moderate-to-severe sleep-disordered breathing and greater obstructive respiratory events were independently correlated with an overnight increase in CAVI (P = 0.033 and P = 0.042, respectively). In patients hospitalized for acute heart failure, arterial stiffness, as assessed by CAVI, significantly increased overnight. Moderate-to-severe sleep-disordered breathing and obstructive respiratory events may play an important role in the overnight increase in cardio-ankle vascular index.

Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis.

Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models). We trained a deep neural network event detection model on 1653 individual recordings and tested the optimized model on 1000 separate hold-out recordings. F1 scores for the optimized joint detection model were 0.70, 0.63, and 0.62 for arousals, leg movements, and sleep disordered breathing, respectively, compared to 0.65, 0.61, and 0.60 for the optimized single-event models. Index values computed from detected events correlated positively with manual annotations (r2 = 0.73, r2 = 0.77, r2 = 0.78, respectively). We furthermore quantified model accuracy based on temporal difference metrics, which improved overall by using the joint model compared to single-event models. Our automatic model jointly detects arousals, leg movements and sleep disordered breathing events with high correlation with human annotations. Finally, we benchmark against previous state-of-the-art multi-event detection models and found an overall increase in F1 score with our proposed model despite a 97.5% reduction in model size. Source code for training and inference is available at https://github.com/neergaard/msed.git.