An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep.

This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

Recognition of apnea and hypopnea by non-contact optical fiber mattress and its application in the diagnosis of obstructive sleep apnea hypopnea syndrome: a retrospective study.

Objectives: This study sought to evaluate the diagnostic value of a non-contact optical fiber mattress for apnea and hypopnea and compare it with traditional polysomnography (PSG) in adult obstructive sleep apnea hypopnea syndrome (OSAHS). Methods: To determine the value of a non-contact optical fiber mattress for apnea and hypopnea, six healthy people and six OSAHS patients were selected from Tongji Hospital to design a program to identify apnea or hypopnea. A total of 108 patients who received polysomnography for drowsiness, snoring or other suspected OSAHS symptoms. All 108 patients were monitored with both the non-contact optical fiber mattress and PSG were collected. Results: Six healthy controls and six patients with OSAHS were included. The mean apnea of the six healthy controls was 1.22 times/h, and the mean hypopnea of the six healthy controls was 2 times/h. Of the six patients with OSAHS, the mean apnea was 12.63 times/h, and the mean hypopnea was 19.25 times/h. The non-contact optical fiber mattress results showed that the mean apnea of the control group was 3.17 times/h and the mean hypopnea of the control group was 3.83 times/h, while the mean apnea of the OSAHS group was 11.95 times/h and the mean hypopnea of the OSAHS group was 17.77 times/h. The apnea index of the non-contact optical fiber mattress was positively correlated with the apnea index of the PSG (P < 0.05, r = 0.835), and the hypopnea index of the non-contact optical fiber mattress was also positively correlated with the hypopnea index of the PSG (P < 0.05, r = 0.959). The non-contact optical fiber mattress had high accuracy (area under curve, AUC = 0.889), specificity (83.4%) and sensitivity (83.3%) for the diagnosis of apnea. The non-contact fiber-optic mattress also had high accuracy (AUC = 0.944), specificity (83.4%) and sensitivity (100%) for the diagnosis of hypopnea. Among the 108 patients enrolled, there was no significant difference between the non-contact optical fiber mattress and the polysomnography monitor in total recording time, apnea hypopnea index (AHI), average heart rate, tachycardia index, bradycardia index, longest time of apnea, average time of apnea, longest time of hypopnea, average time of hypopnea, percentage of total apnea time in total sleep time and percentage of total hypopnea time in total sleep time. The AHI value of the non-contact optical fiber mattress was positively correlated with the AHI value of the PSG (P < 0.05, r = 0.713). The specificity and sensitivity of the non-contact optical fiber mattress AHI in the diagnosis of OSAHS were 95% and 93%, with a high OSAHS diagnostic accuracy (AUC = 0.984). Conclusion: The efficacy of the non-contact optical fiber mattress for OSAHS monitoring was not significantly different than PSG monitoring. The specificity of the non-contact optical mattress for diagnosing OSAHS was 95% and its sensitivity was 93%, with a high OSAHS diagnostic accuracy.

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.

Sleep staging algorithm based on smartwatch sensors for healthy and sleep apnea populations.

OBJECTIVE: Sleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. METHOD: In this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. RESULTS: In the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. CONCLUSION: The results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables.

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.

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease.

STUDY OBJECTIVES: Wearable devices that monitor sleep stages and heart rate offer the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD. METHODS: Ten participants with manifest HD wore an FB during overnight hospital-based PSG, and 9 of these participants continued to wear the FB for 7 nights at home. Sleep stages (30-second epochs) and minute-by-minute heart rate were extracted and compared against PSG data. RESULTS: FB-estimated total sleep and wake times and sleep stage times were in good agreement with PSG, with intraclass correlations of 0.79-0.96. However, poor agreement was observed for wake after sleep onset and the number of awakenings. FB detected waking with 68.6 ± 15.5% sensitivity and 93.7 ± 2.5% specificity, rapid eye movement sleep with high sensitivity and specificity (78.7 ± 31.9%, 95.6 ± 2.3%), and deep sleep with lower sensitivity but high specificity (56.4 ± 28.8%, 95.0 ± 4.8%). FB heart rate was strongly correlated with PSG, and the mean absolute error between FB and PSG heart rate data was 1.16 ± 0.42 beats/min. At home, longer sleep and shorter wake times were observed compared with hospital data, whereas percentage sleep stage times were consistent with hospital data. CONCLUSIONS: Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD. CITATION: Doheny EP, Renerts K, Braun A, et al. Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease. J Clin Sleep Med. 2024;20(7):1163-1171.

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.

Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing.

PURPOSE: This study aimed to develop an unobtrusive method for home sleep apnea testing (HSAT) utilizing micromotion signals obtained by a piezoelectric rubber sheet sensor. METHODS: Algorithms were designated to extract respiratory and ballistocardiogram components from micromotion signals and to detect respiratory events as the characteristic separation of the fast envelope of the respiration component from the slow envelope. In 78 adults with diagnosed or suspected sleep apnea, micromotion signal was recorded with a piezoelectric rubber sheet sensor placed beneath the bedsheet during polysomnography. In a half of the subjects, the algorithms were optimized to calculate respiratory event index (REI), estimating apnea-hypopnea index (AHI). In the other half of subjects, the performance of REI in classifying sleep apnea severity was evaluated. Additionally, the predictive value of the frequency of cyclic variation in heart rate (Fcv) obtained from the ballistocardiogram was assessed. RESULTS: In the training group, the optimized REI showed a strong correlation with the AHI (r = 0.93). Using the optimal cutoff of REI ≥ 14/h, subjects with an AHI ≥ 15 were identified with 77.8% sensitivity and 90.5% specificity. When applying this REI to the test group, it correlated closely with the AHI (r = 0.92) and identified subjects with an AHI ≥ 15 with 87.5% sensitivity and 91.3% specificity. While Fcv showed a modest correlation with AHI (r = 0.46 and 0.66 in the training and test groups), it lacked independent predictive power for AHI. CONCLUSION: The analysis of respiratory component of micromotion using piezoelectric rubber sheet sensors presents a promising approach for HSAT, providing a practical and effective means of estimating sleep apnea severity.

Long-term night-to-night variability of sleep-disordered breathing using a radar-based home sleep apnea test: a prospective cohort study.

STUDY OBJECTIVES: Night-to-night variability of sleep-disordered breathing limits the diagnostic accuracy of a single measurement. Multiple recordings using a reliable, affordable method could reduce the uncertainty and avoid misdiagnosis, which could be possible with radar-based home sleep apnea testing (HSAT). METHODS: We recruited consecutive patients with suspected sleep-disordered breathing and performed contactless radar-based HSAT with automated scoring (Sleepiz One; Sleepiz AG, Zurich, Switzerland) over 10 nights. During the first night, patients were simultaneously measured with peripheral arterial tonometry. RESULTS: Twenty-four of the 28 included patients could achieve a minimum of 4 measurements. The failure rate was 16% (37 of 238 measurements). The apnea-hypopnea index (AHI) and oxygen desaturation index were consistently lower with radar-based HSAT compared with peripheral arterial tonometry. The variability of the AHI was considerable, with a standard error of measurement of 5.2 events/h (95% confidence interval [CI]: 4.6-5.7 events/h) and a minimal detectable difference of 14.4 events/h (95% CI: 12.7-15.9 events/h). Alcohol consumption partially accounted for the variability, with an AHI increase of 1.7 events/h (95% CI: 0.6-2.8 events/h) for each standard drink. Based on a single measurement, 17% of patients were misdiagnosed and 32% were misclassified for sleep-disordered breathing severity. After 5 measurements, the mean AHI of the measured nights stabilized with no evidence of substantial changes with additional measurements. CONCLUSIONS: Night-to-night variability is considerable and stable over 10 nights. HSAT using radar-based methods over multiple nights is feasible and well tolerated by patients. It could offer lower costs and allow for multiple-night testing to increase accuracy. However, validation and reducing the failure rate are necessary for implementation in the clinical routine. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Recording of Multiple Nights Using a New Contactless Device (Sleepiz One Connect) in Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/study/NCT05134402; Identifier: NCT05134402. CITATION: Tschopp S, Borner U, Caversaccio M, Tschopp K. Long-term night-to-night variability of sleep-disordered breathing using a radar-based home sleep apnea test: a prospective cohort study. J Clin Sleep Med. 2024;20(7):1079-1086.

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.

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.

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.

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.