Abstract: SummaryStudy Objectives Sleep disruption is increasingly recognized in hospitalized patients. Impaired sleep is associated with measureable alterations in neurodevelopment. The neonatal intensive care unit (NICU) environment has the potential to affect sleep quality and quantity. We aimed: (i) to determine the frequency and duration of hands-on care, and its impact on sleep, for NICU patients; and (ii) to assess the incidence of respiratory events associated with handling for a cohort of sick neonates. Methods Term and near-term neonates admitted to the NICU and at risk for cerebral dysfunction due to severity of illness or clinical suspicion for seizures underwent attended, bedside polysomnography. Continuous polysomnogram segments were analyzed and data on handling, infant behavioral state, and associated respiratory events were recorded. Results Video and polysomnography data were evaluated for 25 infants (gestational age 39.4 ± 1.6 weeks). The maximum duration between handling episodes for each infant was 50.9 ± 26.2 min, with a median of 2.3 min between contacts. Handling occurred across all behavioral states (active sleep 29.5%; quiet sleep 23.1%; awake 29.9%; indeterminate 17.4%; P = 0.99). Arousals or awakenings occurred in 57% of contacts with a sleeping infant. Hypopnea, apnea, and oxygen desaturation occurred with 16%, 8%, and 19.5% of contacts, respectively. Hypopnea was most likely to occur following contact with infants in active sleep (28%; P < 0.001). Conclusions Infants in the NICU experience frequent hands-on care, associated with disturbances of sleep and respiration. The potential health and developmental impact of these disturbances merits study, as strategies to monitor sleep and minimize sleep-disordered breathing might then improve NICU outcomes. Pediatr Pulmonol. 2017;52:84–90 © 2016 Wiley Periodicals, Inc.

Abstract: Obstructive sleep apnea (OSA) occurs frequently in people with Down syndrome (DS) with reported prevalences ranging between 55% and 97%, compared to 1-4% in the neurotypical pediatric population. Sleep studies are often uncomfortable, costly, and poorly tolerated by individuals with DS. The objective of this study was to construct a tool to identify individuals with DS unlikely to have moderate or severe sleep OSA and in whom sleep studies might offer little benefit. An observational, prospective cohort study was performed in an outpatient clinic and overnight sleep study center with 130 DS patients, ages 3-24 years. Exclusion criteria included previous adenoid and/or tonsil removal, a sleep study within the past 6 months, or being treated for apnea with continuous positive airway pressure. This study involved a physical examination/medical history, lateral cephalogram, 3D photograph, validated sleep questionnaires, an overnight polysomnogram, and urine samples. The main outcome measure was the apnea-hypopnea index. Using a Logic Learning Machine, the best model had a cross-validated negative predictive value of 73% for mild obstructive sleep apnea and 90% for moderate or severe obstructive sleep apnea; positive predictive values were 55% and 25%, respectively. The model included variables from survey questions, medication history, anthropometric measurements, vital signs, patient's age, and physical examination findings. With simple procedures that can be collected at minimal cost, the proposed model could predict which patients with DS were unlikely to have moderate to severe obstructive sleep apnea and thus may not need a diagnostic sleep study.

Abstract: The assessment and management of sleep are increasingly recommended in the clinical practice. Polysomnography (PSG) is considered the gold standard test to monitor sleep objectively, but some practical and technical constraints exist due to environmental and patient considerations. Bispectral index (BIS) monitoring is commonly used in clinical practice for guiding anesthetic administration and provides an index based on relationships between EEG components. Due to similarities in EEG synchronization between anesthesia and sleep, several studies have assessed BIS as a sleep monitor with contradictory results. The aim of this study was to evaluate objectively both the feasibility and reliability of BIS for sleep monitoring through a robust methodology, which included full PSG recordings at a baseline situation and after 40 h of sleep deprivation. Results confirmed that the BIS index was highly correlated with the hypnogram (0.89 ± 0.02), showing a progressive decrease as sleep deepened, and an increase during REM sleep (awake: 91.77 ± 8.42; stage N1: 83.95 ± 11.05; stage N2: 71.71 ± 11.99; stage N3: 42.41 ± 9.14; REM: 80.11 ± 8.73). Mean and median BIS values were lower in the post-deprivation night than in the baseline night, showing statistical differences for the slow wave sleep (baseline: 42.41 ± 9.14 vs. post-deprivation: 39.49 ± 10.27; p = 0.02). BIS scores were able to discriminate properly between deep (N3) and light (N1, N2) sleep. BIS values during REM overlapped those of other sleep stages, although EMG activity provided by the BIS monitor could help to identify REM sleep if needed. In conclusion, BIS monitors could provide a useful measure of sleep depth in especially particular situations such as intensive care units, and they could be used as an alternative for sleep monitoring in order to reduce PSG-derived costs and to increase capacity in ambulatory care.

Abstract: Obstructive sleep apnea (OSA) is a sleep disorder with long-term consequences. Long-term effects include sleep related issues and cardiovascular diseases. OSA is often diagnosed with an overnight sleep test called a polysomnogram. Monitoring can be costly with long wait times for diagnosis and computer-based efficient algorithms are needed. Here, we employ a multi-modal approach that performs feature-level fusion of two physiological signals, namely electrocardiograph (ECG) and saturation of peripheral oxygen (SpO2) for efficient OSA classification. We design Naive Bayes (NB), k-nearest neighbor (kNN), and Support Vector Machine (SVM) classifiers as the learning algorithms and present extensive empirical information regarding the utilized fusion strategy. Compared with other existing methods either considering single modality of signals or perform tests on subjects that have same severity of sleep apnea (i.e., high degree of apnea, low degree of apnea, or without apnea), we also define a test scenario that employs different subjects that have different sleep apnea severity to show the effectiveness of our approach. Our experimental results on real clinical examples from PhysioNet database show that, the proposed multimodal approach using feature-level fusion approach gives best classification rates when using SVM with an average accuracy of 96.64% for all test scenarios, i.e., within Subject with Same Severity (99.49%), between subjects with same sleep apnea severity (95.35%), and between subjects with distinct sleep apnea severity (95.07%).

Abstract: This paper presents a study on identifying sleep apnoea using the photoplethysmography (PPG) measurements, which is obtained from the SpO2 sensor. Using a database of polysomnogram (PSG) records of 52 patients, the heart rate and breathing effort information was derived from the PPG measurements and then features are extracted and processed by a classifier to detect one-minute epochs of sleep apnoea. The ground truth labels for the epochs were determined by trained technicians using the full PSG signal. Pulse oximetry (SpO2) measurements from the same sensor are also used in the classification process for comparison and in combination with the PPG results. The results show that both the heart rate and respiratory effort information derived from the PPG signal were able to detect apnoeic epochs with some success. The best classification performance of 87% for correctly labelling the epochs was obtained when the SpO2 features and the PPG features were combined.

Abstract: The primary metric extracted from the polysomnogram in patients with sleep apnea is the apnea-hypopnea index (or respiratory disturbance index) and its derivatives. Other phenomena of possible importance such as periods of stable breathing, features suggestive of high respiratory control loop gain, and sleep fragmentation phenotypes are not commonly generated in clinical practice or research. A broader phenotype designation can provide insights into biological processes, and possibly clinical therapy outcome effects. The dataset used for this study was the archived baseline diagnostic polysomnograms from the Apnea Positive Pressure Long-term Efficacy Study (APPLES). The electrocardiogram (ECG)-derived cardiopulmonary coupling sleep spectrogram was computed from the polysomnogram. Sleep fragmentation phenotypes used thresholds of sleep efficiency (SE) ≤ 70%, non-rapid eye movement (NREM) sleep N1 ≥ 30%, wake after sleep onset (WASO) ≥ 60 min, and high frequency coupling (HFC) on the ECG-spectrogram ≤ 30%. Sleep consolidation phenotypes used thresholds of SE ≥ 90%, WASO ≤ 30 min, HFC ≥ 50% and N1 ≤ 10%. Multiple and logistic regression analysis explored cross-sectional associations with covariates and across phenotype categories. NREM vs. REM dominant apnea categories were identified when the NREM divided by REM respiratory disturbance index (RDI) was > 1. The data was binned first into mild, moderate, severe and extreme categories based on the respiratory disturbance index of < 10, 10–30, 30–60, and greater than 60, per hour of sleep. Using these criteria, 70, 394, 320 and 188 for polysomnogram, and 54, 296, 209 and 112 subjects for ECG-spectrogram analysis groups. All phenotypes were seen at all severity levels. There was a higher correlation of NREM-RDI with the amount of ECG-spectrogram narrow band coupling, vs. REM-RDI, 0.41 vs 0.14, respectively. NREM dominance was associated with male gender and higher mixed/central apnea indices. Absence of the ECG-spectrogram sleep consolidated phenotype was associated with an increased odds of being on antihypertensive medications, OR 2.65 [CI: 1.64–4.26], p = < 0.001. Distinct phenotypes are readily seen at all severities of sleep apnea, and can be identified from conventional polysomnography. The ECG-spectrogram analysis provides further phenotypic differentiation.

Abstract: Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

Abstract: Obstructive sleep apnea-hypopnea syndrome (OSAHS) requires a thorough medical evaluation and diagnosis confirmation, as well as the estimation of its severity using diagnostic means. These conditions are fulfilled by the basal polysomnogram, which monitors sleep throughout the night; this is a standardized study that requires minimum quality parameters that must be met in all cases. The multiple sleep latency test has been standardized with due care and is indicated for quantifying excessive daytime sleepiness. On the other hand, positive airway pressure titration by polysomnography allows to find the minimum therapeutic pressure to correct all obstructive respiratory events. The split-night polysomnogram tries to improve appointment opportunities, which contrasts with the studies mentioned above, since both of them can be done in one night, although, it has its own limitations. Home sleep studies are classified according to their level of complexity and care; they seek to diminish the opportunity of appointments and are considered as screening studies. In addition, the psychomotor vigilance test is used to control therapies focused on improving excessive daytime sleepiness.

Abstract: Sleep is necessary for the best physical and mental health. As we age, the amount or quality of sleep we get may change. Sleep disorders are more common in older people. In addition, sleeping problems occur more often in those with cognitive or thinking problems like dementia. Research shows a connection between sleep disorders and cognitive problems or dementia in older adults.1 Sleep disorders are often evaluated with a test called polysomnography. Polysomnography is an overnight sleep test that checks breathing patterns, oxygen levels, pulse, and brain waves. Few studies have used these detailed measures to assess sleep. Instead, less reliable tests like surveys are often used. Dr. Haba-Rubio and coauthors2 studied both subjective data (survey) and objective data (polysomnography) to see if sleep and cognition are somehow related. The study was done in Lausanne, Switzerland, between 2003 and 2006. The researchers gathered information from 580 patients older than 65 years. Sleep patterns were evaluated by surveys given to patients. Polysomnograms were also used. The polysomnogram recorded a full night's sleep at home and showed possible …

Abstract: We evaluated the possibility that in some patients with obstructive sleep apnea, the initial polysomnogram may be negative. We reviewed polysomnograms performed at the Medical College of Georgia from 1984 to 1990 and found nine patients whose initial polysomnogram was negative but whose repeat polysomnogram confirmed obstructive sleep apnea. All nine patients (five women and four men; average age, 44.2 years) had an apnea index of less than 5 (fewer than five apneic episodes per hour) and had a total offewer than 20 apneic episodes during the initial overnight

Abstract: Patients with Down syndrome (DS) are at risk for both obstructive sleep apnea (OSA) and central sleep apnea (CSA); however, it is unclear how these components evolve as patients age and whether patients are also at risk for hypoventilation. A retrospective review of 144 diagnostic polysomnograms (PSG) in a tertiary care facility over 10 years was conducted. Descriptive data and exploratory correlation analyses were performed. Sleep disordered breathing was common (seen in 78% of patients) with an average apnea-hypopnea index (AHI) = 10. The relative amount of obstructive apnea was positively correlated with age and body mass index (BMI). The relative amount of central sleep apnea was associated with younger age in the very youngest group (0–3 years). Hypoventilation was common occurring in more than 22% of patients and there was a positive correlation between the maximum CO2 and BMI. Sleep disordered breathing, including hypoventilation, was common in patients with DS. The obstructive component increased significantly with age and BMI, while the central component occurred most in the very young age group. Due to the high risk of hypoventilation, which has not been previously highlighted, it may be helpful to consider therapies to target both apnea and hypoventilation in this population.

Abstract: Purpose of reviewRecent advances in diagnostic testing for obstructive sleep apnea in children have refined the standard tests while identifying several new tools that hold promise to radically change how we diagnose sleep apnea.Recent findingsStudies have demonstrated that the polysomnogram may be

Abstract: Study Objectives:Our aim was to identify clinical predictors associated with changes in settings for pediatric invasive and noninvasive positive airway pressure therapy, which could help inform the...