Artificial intelligence augmented home sleep apnea testing device study (AISAP study).

STUDY OBJECTIVE: This study aimed to prospectively validate the performance of an artificially augmented home sleep apnea testing device (WVU-device) and its patented technology. METHODOLOGY: The WVU-device, utilizing patent pending (US 20210001122A) technology and an algorithm derived from cardio-pulmonary physiological parameters, comorbidities, and anthropological information was prospectively compared with a commercially available and Center for Medicare and Medicaid Services (CMS) approved home sleep apnea testing (HSAT) device. The WVU-device and the HSAT device were applied on separate hands of the patient during a single night study. The oxygen desaturation index (ODI) obtained from the WVU-device was compared to the respiratory event index (REI) derived from the HSAT device. RESULTS: A total of 78 consecutive patients were included in the prospective study. Of the 78 patients, 38 (48%) were women and 9 (12%) had a Fitzpatrick score of 3 or higher. The ODI obtained from the WVU-device corelated well with the HSAT device, and no significant bias was observed in the Bland-Altman curve. The accuracy for ODI > = 5 and REI > = 5 was 87%, for ODI> = 15 and REI > = 15 was 89% and for ODI> = 30 and REI of > = 30 was 95%. The sensitivity and specificity for these ODI /REI cut-offs were 0.92 and 0.78, 0.91 and 0.86, and 0.94 and 0.95, respectively. CONCLUSION: The WVU-device demonstrated good accuracy in predicting REI when compared to an approved HSAT device, even in patients with darker skin tones.

Age related disparities in sleep apnea diagnosis using a wearable device: Implications of 4% vs. 3% hypopnea scoring criteria.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) diagnosis relies on the Apnea-Hypopnea Index (AHI), with discrepancies arising from the 3% and 4% desaturation criteria. This study investigates age-related variations in OSA severity classification, utilizing data from 1201 adult patients undergoing Home Sleep Apnea Testing (HSAT) with SleepImage Ring@. METHODS: The study employs Bland-Altman analysis to compare AHI values obtained with the 3% and 4% desaturation criteria. Age-stratified analysis explores discrepancies across different age groups. RESULTS: The analysis reveals a systematic bias favoring the 3% criterion, impacting the quantification of apnea events. Age-specific patterns demonstrate diminishing agreement between criteria with increasing age. CONCLUSION: This comprehensive study underscores the importance of standardized criteria in OSA diagnosis. The findings emphasize age-specific considerations and ethical concerns, providing crucial insights for optimizing patient care and advancing sleep medicine practices.

Application of portable sleep monitoring devices in pregnancy: a comprehensive review.

Objective.The physiological, hormonal and biomechanical changes during pregnancy may trigger sleep disordered breathing (SDB) in pregnant women. Pregnancy-related sleep disorders may associate with adverse fetal and maternal outcomes including gestational diabetes, preeclampsia, preterm birth and gestational hypertension. Most of the screening and diagnostic studies that explore SDB during pregnancy were based on questionnaires which are inherently limited in providing definitive conclusions. The current gold standard in diagnostics is overnight polysomnography (PSG) involving the comprehensive measurements of physiological changes during sleep. However, applying the overnight laboratory PSG on pregnant women is not practical due to a number of challenges such as patient inconvenience, unnatural sleep dynamics, and expenses due to highly trained personnel and technology. Parallel to the progress in wearable sensors and portable electronics, home sleep monitoring devices became indispensable tools to record the sleep signals of pregnant women at her own sleep environment. This article reviews the application of portable sleep monitoring devices in pregnancy with particular emphasis on estimating the perinatal outcomes.Approach.The advantages and disadvantages of home based sleep monitoring systems compared to subjective sleep questionnaires and overnight PSG for pregnant women were evaluated.Main Results.An overview on the efficiency of the application of home sleep monitoring in terms of accuracy and specificity were presented for particular fetal and maternal outcomes.Significance.Based on our review, more homogenous and comparable research is needed to produce conclusive results with home based sleep monitoring systems to study the epidemiology of SDB in pregnancy and its impact on maternal and neonatal health.

Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy.

STUDY OBJECTIVES: The aim of this study was to develop a sleep staging classification model capable of accurately performing on different wearable devices. METHODS: Twenty-three healthy participants underwent a full-night type I polysomnography and used two device combinations: (A) flexible single-channel electroencephalogram (EEG) headband + actigraphy (n = 12) and (B) rigid single-channel EEG headband + actigraphy (n = 11). The signals were segmented into 30-second epochs according to polysomnographic stages (scored by a board-certified sleep technologist; model ground truth) and 18 frequency and time features were extracted. The model consisted of an ensemble of bagged decision trees. Bagging refers to bootstrap aggregation to reduce overfitting and improve generalization. To evaluate the model, a training dataset under 5-fold cross-validation and an 80-20% dataset split was used. The headbands were also evaluated without the actigraphy feature. Participants also completed a usability evaluation (comfort, pain while sleeping, and sleep disturbance). RESULTS: Combination A had an F1-score of 98.4% and the flexible headband alone of 97.7% (error rate for N1: combination A = 9.8%; flexible headband alone = 15.7%). Combination B had an F1-score of 96.9% and the rigid headband alone of 95.3% (error rate for N1: combination B = 17.0%; rigid headband alone = 27.7%); in both, N1 was more confounded with N2. CONCLUSIONS: We developed an accurate sleep classification model based on a single-channel EEG device, and actigraphy was not an important feature of the model. Both headbands were found to be useful, with the rigid one being more disruptive to sleep. Future research can improve our results by applying the developed model in a population with sleep disorders. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging; URL: https://clinicaltrials.gov/study/NCT04943562; Identifier: NCT04943562. CITATION: Melo MC, Vallim JRS, Garbuio S, et al. Validation of a sleep staging classification model for healthy adults based on 2 combinations of a single-channel EEG headband and wrist actigraphy. J Clin Sleep Med. 2024;20(6):983-990.

Is it Time to Include Wearable Sleep Trackers in the Applied Psychologists’ Toolbox?

Wearable sleep trackers are increasingly used in applied psychology. Particularly, the recent boom in the fitness tracking industry has resulted in a number of relatively inexpensive consumer-oriented devices that further enlarge the potential applications of ambulatory sleep monitoring. While being largely positioned as wellness tools, wearable sleep trackers could be considered useful health devices supported by a growing number of independent peer-reviewed studies evaluating their accuracy. The inclusion of sensors that monitor cardiorespiratory physiology, diurnal activity data, and other environmental signals allows for a comprehensive and multidimensional approach to sleep health and its impact on psychological well-being. Moreover, the increasingly common combination of wearable trackers and experience sampling methods has the potential to uncover within-individual processes linking sleep to daily experiences, behaviors, and other psychosocial factors. Here, we provide a concise overview of the state-of-the-art, challenges, and opportunities of using wearable sleep-tracking technology in applied psychology. Specifically, we review key device profiles, capabilities, and limitations. By providing representative examples, we highlight how scholars and practitioners can fully exploit the potential of wearable sleep trackers while being aware of the most critical pitfalls characterizing these devices. Overall, consumer wearable sleep trackers are increasingly recognized as a valuable method to investigate, assess, and improve sleep health. Incorporating such devices in research and professional practice might significantly improve the quantity and quality of the collected information while opening the possibility of involving large samples over representative time periods. However, a rigorous and informed approach to their use is necessary. (AU)

Comparación entre el análisis automático y manual de 301 poligrafías respiratorias domiciliarias con ApneaLink / Comparison between automatic and manual scoring of 301 home respiratory polygraphs with ApneaLink device

Resumen Introducción: El síndrome de apnea obstructiva del sueño (SAOS) se asocia a aumento de morbimortalidad cardiovascular y metabólica, y a mala calidad de vida. Su diagnóstico y tratamiento eficaz mejora la salud individual y pública. Objetivo: evaluar concordancia entre análisis automático versus manual del dispositivo ApneaLink para diagnosticar y clasificar SAOS en pacientes con sospecha clínica. Material y Método: Evaluación retrospectiva de 301 poligrafías respiratorias del HOSCAR. Se mide correlación, acuerdo general y concordancia entre parámetros obtenidos manual y automáticamente usando coeficiente de Pearson, coeficiente de correlación intraclase y gráfico de Bland y Altman. Resultados: En 11,3% de casos el análisis automático interpreto erróneamente la señal de flujo. No hubo diferencias significativas entre índices de apnea-hipopnea automático (AHIa 18,9 ± 17,5) y manual (AHIm 20,8 ± 19,4) r + 0,97 (95% CI: 0,9571 a 0,9728; p < 0,0001) y tampoco entre la saturación mínima de oxígeno automática (82,1 ± 7,6) y manual (83,1 ± 6,8) r + 0,85 (95% CI: 0,8108 a 0,8766; p < 0,0001). No hubo buena correlación entre análisis automático y manual en clasificación de apneas centrales, r + 0,51 (95% CI: 0,4238 a 0,5942; p < 0,0001). Hubo subestimación de gravedad de SAOS por análisis automático: en 11% de casos. Conclusión: El diagnóstico entregado automáticamente por ApneaLink podría aceptarse sin confirmación manual adicional solamente en casos clasificados como severos. Para AHI menores se requeriría confirmación mediante análisis manual de experto.

Abstract Introduction: Obstructive sleep apnea syndrome (OSAS) is associated with increased cardiovascular and metabolic morbidity and mortality, and poor quality of life. Its effective diagnosis and treatment improve individual and public health. Aim: To evaluate concordance between automatic versus manual analysis of the ApneaLink device to diagnose and classify OSAS in patients with clinical suspicion. Material and Method: Retrospective evaluation of 301 respiratory polygraphs from HOSCAR. Correlation, general agreement and concordance between parameters obtained manually and automatically are measured using Pearson's coefficient, intraclass correlation coefficient, and Bland and Altman graph. Results: In 11.3% of cases, the automatic analysis misinterpreted the flow signal. There were no significant differences between automatic (AHIa 18.9 ± 17.5) and manual (AHIm 20.8 ± 19.4) apnea-hypopnea indices r + 0.97 (95% CI:0.9571 to 0.9728, p < 0.0001) and nor between automatic (82.1 ± 7.6) and manual (83.1 ± 6.8) minimum oxygen saturation r + 0.85 (95% CI: 0.8108 to 0.8766, p < 0.0001). There was no good correlation between automatic and manual analysis in the classification of central apneas, r + 0.51(95% CI:0.4238 to 0.5942, p < 0.0001). There was an underestimation of the severity of OSAS by automatic analysis in 11% of cases. Conclusion: The diagnosis delivered automatically by ApneaLink could be accepted without additional manual confirmation only in cases classified as severe. For minors AHI, confirmation through manual expert analysis would be required.

Home sleep apnea testing: an accuracy study.

AIM: There are no studies comparing tests performed at home with those carried out in the laboratory, using the same device. The only studies that have been performed have compared the device used at home with the standard polygraph used in the laboratory. The purpose of this study was therefore to verify the accuracy of the home diagnosis of obstructive sleep apnea syndrome (OSAS) via unassisted type 2 portable polysomnography, compared with polysomnography using the same equipment in a sleep laboratory. METHODS: To avoid any possible order effect on the apnea-hypopnea index (AHI), we randomly created two groups of 20-total 40 patients, according to the test sequence. One of the groups had the first test at home and the second test in the laboratory (H-L); the other group had the first test in the laboratory and the second at home (L-H). The second test always took place on the night immediately following the first test. All polysomnographic monitoring was undertaken with the same equipment, an Embletta X100 system (Embla, Natus Inc., Middleton, USA). The Embletta X100 is a portable polygraph that records eleven polygraph signs: (1) electroencephalogram C4/A; (2) electroencephalogram O2/M1; (3) submental EMG; (4) electrooculogram of the right side; (5) nasal cannula (air flow); (6) respiratory effort against a plethysmographic chest strap; (7) respiratory effort against an abdominal plethysmographic belt; (8) heart rate; (9) saturation of oxyhemoglobin; (10) snoring; and (11) body position. RESULTS: There was no difference in sleep efficiency between the group monitored in the laboratory and the group tested at home (p = 0.30). There was no difference in total sleep time (p = 0.11) or sleep latency (p = 0.52), or in the latency in phases N2 and N3 between the monitoring in the laboratory and at home (N2 p = 0.24; N3 p = 0.09). Some differences occurred regarding the PSG that took place at home, with longer duration of wake after sleep onset (WASO) and longer latency for REM sleep, due to failure of the patient to start the monitoring by pressing the "events" button on the device. In the distribution of sleep phases, there was no difference between the group monitored in the laboratory and the group tested at home. CONCLUSION: Results from home sleep monitoring correlate well with the laboratory "gold standard" and may be an option for diagnosing OSAS in selected patients.

Development of an IoT-Based Sleep Apnea Monitoring System for Healthcare Applications.

Sleep is an essential and vital element of a person's life and health that helps to refresh and recharge the mind and body of a person. The quality of sleep is very important in every person's lifestyle, removing various diseases. Bad sleep is a big problem for a lot of people for a very long time. People suffering from various diseases are dealing with various sleeping disorders, commonly known as sleep apnea. A lot of people die during sleep because of uneven body changes in the body during sleep. On that note, a system to monitor sleep is very important. Most of the previous systems to monitor sleeping problems cannot deal with the real time sleeping problem, generating data after a certain period of sleep. Real-time monitoring of sleep is the key to detecting sleep apnea. To solve this problem, an Internet of Things- (IoT-) based real-time sleep apnea monitoring system has been developed. It will allow the user to measure different indexes of sleep and will notify them through a mobile application when anything odd occurs. The system contains various sensors to measure the electrocardiogram (ECG), heart rate, pulse rate, skin response, and SpO2 of any person during the entire sleeping period. This research is very useful as it can measure the indexes of sleep without disturbing the person and can also show it in the mobile application simultaneously with the help of a Bluetooth module. The system has been developed in such a way that it can be used by every kind of person. Multiple analog sensors are used with the Arduino UNO to measure different parameters of the sleep factor. The system was examined and tested on different people's bodies. To analyze and detect sleep apnea in real-time, the system monitors several people during the sleeping period. The results are displayed on the monitor of the Arduino boards and in the mobile application. The analysis of the achieved data can detect sleep apnea in some of the people that the system monitored, and it can also display the reason why sleep apnea happens. This research also analyzes the people who are not in the danger of sleeping problems by the achieved data. This paper will help everyone learn about sleep apnea and will help people detect it and take the necessary steps to prevent it.

Is the Nox-T3 device scoring algorithm accurate enough for the diagnosis of obstructive sleep apnea?

INTRODUCTION: Obstructive sleep apnea (OSA) is highly prevalent. Home sleep apnea testing (HSAT) for OSA is rapidly expanding because of its cost effectiveness in the diagnosis of OSA. Type 3 portable monitors are used for this purpose. In most cases, these devices contain an algorithm for automatic scoring of events. We propose to study the accuracy of the automatic scoring algorithm in our population in order to compare it with the manually edited scoring of Nox-T3®. MATERIAL AND METHODS: For five months, a prospective study was performed. Patients were randomly distributed to the available HSAT devices. We collected the data of patients who performed HSAT with Nox-T3®. We used normality plots, the Spearman correlation, the Wilcoxon signed-rank test, and Bland-Altman plots. RESULTS: The sample consisted of 283 participants. The average manual apnea and hypopnea index (AHI) was 23.7 ± 22.1 events/h. All manual scores (AHI, apnea index, hypopnea index, and oxygen desaturation index) had strong correlations with their respective automated scores. When AHI > 15 and AHI > 30 the difference between the values of this index (automatic and manual) was not statistically significant. Also, for AHI values > 15 the mean difference between the two scoring methods was 0.17 events/h. For AHI values > 30, this difference was - 1.23 events/h. CONCLUSIONS: When AHI is < 15, there may be a need for confirmation of automatic scores, especially in symptomatic patients with a high pretest probability of OSA. But, for patients with AHI > 15, automatic scores obtained from this device seem accurate enough to diagnose OSA in the correct clinical setting.

A Systematic Review of Sensing Technologies for Wearable Sleep Staging.

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

Diagnosis of Retrolingual Obstruction during Drug-Induced Sleep Endoscopy versus Polysomnography with Nasopharyngeal Tube in Patients with Obstructive Sleep Apnea.

OBJECTIVE: To compare the retrolingual obstruction during drug-induced sleep endoscopy (DISE) with the retrolingual obstruction during polysomnography with nasopharyngeal tube (NPT-PSG). METHODS: A cross-sectional study of 77 consecutive patients with moderate and severe obstructive sleep apnea (OSA) was conducted. After 15 patients were excluded from the study for not completing DISE or NPT-PSG successfully, 62 patients were included in this study. Retrolingual sites of obstruction grade 2 determined by DISE according to the VOTE (velum, oropharynx lateral wall, tongue base, and epiglottis) classification were considered as retrolingual obstruction, while apnea-hypopnea index (AHI) ≥ 15 events/hour determined by NPT-PSG was considered as retrolingual obstruction. The extent of agreement between DISE and NPT-PSG findings was evaluated using unweighted Cohen's kappa test. RESULTS: The 62 study participants (11 moderate OSA, 51 severe OSA) had a mean (SD) age of 39.8 (9.9) years, and 58 (94%) were men. No statistically significant differences between included and excluded patients were observed in patient characteristics. The extent of agreement in retrolingual obstruction between DISE and NPT-PSG was 80.6% (Cohen k = 0.612; 95% CI, 0.415-0.807). CONCLUSION: Retrolingual obstruction requiring treatment showed good agreement between DISE and NPT-PSG, suggesting that NPT-PSG may also be a reliable method to assess the retrolingual obstruction.

Sleep classification from wrist-worn accelerometer data using random forests.

Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.

The impact of the COVID-19 pandemic on sleep medicine practices.

STUDY OBJECTIVES: The COVID-19 pandemic required sleep centers to consider and implement infection control strategies to mitigate viral transmission to patients and staff. Our aim was to assess measures taken by sleep centers due to the COVID-19 pandemic and plans surrounding reinstatement of sleep services. METHODS: We distributed an anonymous online survey to health care providers in sleep medicine on April 29, 2020. From responders, we identified a subset of unique centers by region and demographic variables. RESULTS: We obtained 379 individual responses, which represented 297 unique centers. A total of 93.6% of unique centers reported stopping all or nearly all sleep testing of at least one type, without significant differences between adult and pediatric labs, geographic region, or surrounding population density. By contrast, a greater proportion of respondents continued home sleep apnea testing services. A total of 60.3% reduced home sleep apnea testing volume by at least 90%, compared to 90.4% that reduced in-laboratory testing by at least 90%. Respondents acknowledged that they implemented a wide variety of mitigation strategies. While no respondents reported virtual visits to be ≥ 25% of clinical visits prior to the pandemic, more than half (51.9%) anticipated maintaining ≥ 25% virtual visits after the pandemic. CONCLUSIONS: Among surveyed sleep centers, the vast majority reported near-cessation of in-laboratory sleep studies, while a smaller proportion reported reductions in home sleep apnea tests. A large increase in the use of telemedicine was reported, with the majority of respondents expecting the use of telehealth to endure in the future.

Assessing the performance of a commercial multisensory sleep tracker.

Wearable sleep technology allows for a less intruding sleep assessment than PSG, especially in long-term sleep monitoring. Though such devices are less accurate than PSG, sleep trackers may still provide valuable information. This study aimed to validate a commercial sleep tracker, Garmin Vivosmart 4 (GV4), against polysomnography (PSG) and to evaluate intra-device reliability (GV4 vs. GV4). Eighteen able-bodied adults (13 females, M = 56.1 ± 12.0 years) with no self-reported sleep disorders were simultaneously sleep monitored by GV4 and PSG for one night while intra-device reliability was monitored in one participant for 23 consecutive nights. Intra-device agreement was considered sufficient (observed agreement = 0.85 ± 0.13, Cohen's kappa = 0.68 ± 0.24). GV4 detected sleep with high accuracy (0.90) and sensitivity (0.98) but low specificity (0.28). Cohen's kappa was calculated for sleep/wake detection (0.33) and sleep stage detection (0.20). GV4 significantly underestimated time awake (p = 0.001) including wake after sleep onset (WASO) (p = 0.001), and overestimated light sleep (p = 0.045) and total sleep time (TST) (p = 0.001) (paired t-test). Sleep onset and sleep end differed insignificantly from PSG values. Our results suggest that GV4 is not able to reliably describe sleep architecture but may allow for detection of changes in sleep onset, sleep end, and TST (ICC ≥ 0.825) in longitudinally followed groups. Still, generalizations are difficult due to our sample limitations.

Quantitative detection of sleep apnea with wearable watch device.

The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4-28.4) and 22 (54%) subjects had AHI ≥15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI ≥15 (19.6 ± 12.3 /h) than in those with AHI <15 (6.4 ± 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.

Technical Tips: Keeping It Clean during COVID-19.

Since 1995, ASET has periodically published updates to recommendations for best practices in infection prevention for Neurodiagnostic technologists. The latest installment was accepted in December 2019 for publication in Volume 60, Issue 1, before we had much knowledge or understanding about the SARS-CoV-2, the virus that causes COVID-19. This Technical Tips article is presented as an addendum to the 2020 update and includes important information about infection prevention measures specific to procedure protocols when working with patients positive or under investigation for a highly infectious disease, and when working with patients in general during the current pandemic. All Neurodiagnostic technologists who have direct patient care are responsible for ensuring the use of best practices to prevent the spread of infection.

PSG Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device.

BACKGROUND: Actigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs. METHODS: Sleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm. RESULTS: Based on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc. CONCLUSIONS: An optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc's ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography.

Night-to-night variability in respiratory parameters in children and adolescents examined for obstructive sleep apnea.

INTRODUCTION: The diagnosis of obstructive sleep apnea (OSA) is routinely based on just a single night's sleep examination. The night-to-night variability in children and adolescents has previously been investigated using type 4 sleep monitors or PSG. However, there is a lack of studies investigating the night-to-night variability when using type 3 sleep monitors. Therefore, the main purpose was to investigate the night-to-night variability in respiratory parameters in children and adolescents using a portable type 3 monitor. Furthermore, the purpose was to investigate the clinical relevance of night-to-night variability. METHODS: The study population was recruited from an ongoing research project concerning the effect of weight loss in children and adolescents with OSA and overweight/obesity. The inclusion criterion was the successful recording of two consecutive nights of sleep. Sleep examinations were recorded at home using the Nox T3 device and then blindly scored by the same registered polysomnographic technologist. To compare the respiratory parameters measured each night, a paired t-test or a Wilcoxon signed-rank test was used. The apnea-hypopnea index (AHI) was further described graphically with a scatter plot and a Bland-Altman plot. The presence and severity of OSA were described in tables. RESULTS: A total of 30 children and adolescents with a median age of 14.8 years were included. When comparing respiratory parameters between nights, all p-values derived from paired t-tests and Wilcoxon signed-rank tests were >0.05. When considering the graphical depictions of AHI, it was evident that for some participants AHI measurements varied widely from night to night. Regarding the presence of OSA, 27% of participants changed diagnostic category between nights and 40% of those with a normal AHI on the first night had OSA on the second night. Regarding OSA severity, 50% of participants changed severity category between nights. CONCLUSIONS: AHI measurements varied widely between nights in some children and adolescents leading to frequent changes in both diagnosis and severity of OSA from night to night. We therefore suggest the presence of a clinically relevant night-to-night variability which should be taken into account when diagnosing pediatric OSA.

Sleep Breathing Disorders in the COVID-19 Era: Italian Thoracic Society Organizational Models for a Correct Approach to Diagnosis and Treatment.

The attenuation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, at least in Italy, allows a gradual resumption of diagnostic and therapeutic activities for sleep respiratory disorders. The knowledge on this new disorder is growing fast, but our experience is still limited and when a physician cannot rely on evidence-based medicine, the experience of his peers can support the decision-making and operational process of reopening sleep laboratories. The aim of this paper is to focus on the safety of patients and operators accessing hospitals and the practice of diagnosing and treating sleep-related respiratory disorders. The whole process requires a careful plan, starting with a triage preceding the access to the facility, to minimize the risk of infection. Preparation of the medical record can be performed through standard questionnaires administered over the phone or by e-mail, including an assessment of the COVID-19 risk. The home sleep test should include single-patient sensors or easy-to-sanitize material. The use of nasal cannulas is discouraged in view of the risk of the virus colonizing the internal reading chamber, since no filter has been tested and certified to be used extensively for coronavirus due to its small size. The adaptation to positive airway pressure (PAP) treatment can also be performed mainly using telemedicine procedures. In the adaptation session, the mask should be new or correctly sanitized and the PAP device, without a humidifier, should be protected by an antibacterial/antiviral filter, then sanitized and reassigned after at least 4 days since SARS-CoV-2 was detected on some surfaces up to 72 h after. Identification of pressure should preferably be performed by telemedicine. The patient should be informed of the risk of spreading the disease in the family environment through droplets and how to reduce this risk. The follow-up phase can again be performed mainly by telemedicine both for problem solving and the collection of data. Public access to hospital should be minimized and granted to patients only. Constant monitoring of institutional communications will help in implementing the necessary recommendations.

Polysomnographically measured sleep changes in idiopathic REM sleep behavior disorder: A systematic review and meta-analysis.

Polysomnographic studies conducted to explore sleep changes in idiopathic rapid eye movement sleep behavior disorder (iRBD) have not established clear relationships between sleep disturbances and iRBD. To explore the polysomnographic differences between iRBD patients and healthy controls and their associated factors, an electronic literature search was conducted in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycINFO inception to December 2019.34 studies were identified for systematic review, 33 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time (SMD = -0.212, 95%CI: -0.378 to -0.046), sleep efficiency (SMD = -0.194, 95%CI: -0.369 to -0.018), apnea hypopnea index (SMD = -0.440, 95%CI: -0.780 to -0.101), and increases in sleep latency (SMD = 0.340, 95%CI: 0.074 to 0.606), and slow wave sleep (SMD = 0.294, 95%CI: 0.064 to 0.523) in iRBD patients compared with controls. Furthermore, electroencephalogram frequency components during REM sleep were altered in iRBD patients compared with controls; however, the specific changes could not be determined. Our findings suggest that polysomnographic sleep is abnormal in iRBD patients. Further studies are needed on underlying mechanisms and associations with neurodegenerative diseases.