Validation of a new wearable device for type 3 sleep test without flowmeter.
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ABSTRACT: Background
Ventilation monitoring during sleep is performed by sleep test instrumentation that is uncomfortable for the patients due to the presence of the flowmeter. The objective of this study was to evaluate if an innovative type 3 wearable system, the X10X and X10Y, is able to correctly detect events of apnea and hypopnea and to classify the severity of sleep apnea without the use of a flowmeter.Methods
40 patients with sleep disordered breathing were analyzed by continuous and simultaneous recording of X10X and X10Y and another certified type 3 system, SOMNOtouch, used for comparison. Evaluation was performed in terms of quality of respiratory signals (scores from 1, lowest, to 5, highest), duration and classification of apneas, as well as identification and duration of hypopneas.Results
580 periods were evaluated. Mean quality assigned score was 3.37±1.42 and 3.25±1.35 for X10X and X10Y and SOMNOtouch, respectively. The agreement between the two systems was evaluated with grades 4 and 5 in 383 out of 580 cases. A high correlation (r2 = 0.921; p<0.001) was found between the AHI indexes obtained from the two systems. X10X and X10Y devices were able to correctly classify 72.3% of the obstructive apneas, 81% of the central apneas, 61.3% of the hypopneas, and 64.6% of the mixed apneas when compared to SOMNOtouch device.Conclusion
The X10X and X10Y devices are able to provide a correct grading of sleep respiratory disorders without the need of a nasal cannula for respiratory flow measurement and can be considered as a type 3 sleep test device for screening tests.
SUBMITTER: Contini M
PROVIDER: S-EPMC8051765 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Study objectivesTo compare the quality and consistency in sleep measurement of a consumer wearable device and a research-grade actigraph with polysomnography (PSG) in adolescents.MethodsFifty-eight healthy adolescents (aged 15-19 years; 30 males) underwent overnight PSG while wearing both a Fitbit Alta HR and a Philips Respironics Actiwatch 2 (AW2) for 5 nights, with either 5 hours or 6.5 hours time in bed (TIB) and for 4 nights with 9 hours TIB. AW2 data were evaluated using two different wake and immobility thresholds. Discrepancies in estimated total sleep time (TST) and wake after sleep onset (WASO) between devices and PSG, as well as epoch-by-epoch agreements in sleep/wake classification, were assessed. Fitbit-generated sleep staging was compared to PSG.ResultsFitbit and AW2 under default settings similarly underestimated TST and overestimated WASO (TST: medium setting (M10) ≤ 38 minutes, Fitbit ≤ 47 minutes; WASO: M10 ≤ 38 minutes; Fitbit ≤ 42 minutes). AW2 at the high motion threshold setting provided readings closest to PSG (TST: ≤ 12 minutes; WASO: ≤ 18 minutes). Sensitivity for detecting sleep was ≥ 90% for both wearable devices and further improved to 95% by using the high threshold (H5) setting for the AW2 (0.95). Wake detection specificity was highest in Fitbit (≥ 0.88), followed by the AW2 at M10 (≥ 0.80) and H5 thresholds (≤ 0.73). In addition, Fitbit inconsistently estimated stage N1 + N2 sleep depending on TIB, underestimated stage N3 sleep (21-46 min), but was comparable to PSG for rapid eye movement sleep. Fitbit sensitivity values for the detection of N1 + N2, N3 and rapid eye movement sleep were ≥ 0.68, ≥ 0.50, and ≥ 0.72, respectively.ConclusionsA consumer-grade wearable device can measure sleep duration as well as a research actigraph. However, sleep staging would benefit from further refinement before these methods can be reliably used for adolescents.Clinical trial registrationRegistry: ClinicalTrials.gov; Title: The Cognitive and Metabolic Effects of Sleep Restriction in Adolescents; Identifier: NCT03333512; URL: https://clinicaltrials.gov/ct2/show/NCT03333512.CitationLee XK, Chee NIYN, Ong JL, Teo TB, van Rijn E, Lo JC, Chee MWL. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med. 2019;15(9):1337-1346.
| S-EPMC6760396 | biostudies-literature
Project description:Ambulatory blood pressure (BP) monitoring is recommended to improve the management of hypertension. Here, we investigated the accuracy of BP estimated using a wearable cuff-less device, InBodyWATCH, compared with BP measured using a manual sphygmomanometer. Thirty-five adults were enrolled (age 57.1 ± 17.9 years). The BP was estimated using InBodyWATCH with an individualized estimation based on a neural network model. Three paired sets of BPs from the two devices were compared using correlation analysis and Bland-Altman plots (n = 105 paired BP readings). The correlations for both systolic and diastolic BP (SBP and DBP) between the two devices were high (r = 0.964 and 0.939, both P < 0.001). The mean difference was 2.2 ± 6.1 mmHg for SBP and -0.2 ± 4.2 mmHg for DBP; these were not significant (P = 0.472 for SBP and P = 0.880 for DBP). The proportions of estimated SBP/DBP obtained from the InBodyWATCH within ± 5 mmHg of manual SBP/DBP were 71.4%/83.8%; within ± 10 mmHg they were 86.7%/98.1%; and within ± 15 mmHg they were 97.1%/99.0%. The estimated BP from this wearable cuff-less device correlated highly with the manual BP and showed good accuracy, suggesting its potential to be used in ambulatory BP monitoring.
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Project description:Study objectivesOur objective was to examine the ability of a consumer-grade wearable device (Basis B1) with accelerometer and heart rate technology to assess sleep patterns compared with polysomnography (PSG) and research-grade actigraphy in healthy adults.MethodsEighteen adults underwent consecutive nights of sleep monitoring using Basis B1, actigraphy, and PSG; 40 nights were used in analyses. Discrepancies in gross sleep parameters and epoch-by-epoch agreements in sleep/wake classification were assessed.ResultsBasis B1 accuracy was 54.20 ± 8.20%, sensitivity was 98.90 ± 2.70%, and specificity was 8.10 ± 15.00%. Accuracy, sensitivity, and specificity for distinguishing between the different sleep stages were 60-72%, 48-62%, and 57-86%, respectively. Pearson correlations demonstrated strong associations between Basis B1 and PSG estimates of sleep onset latency and total sleep time; moderate associations for sleep efficiency, duration of light sleep, and duration of rapid eye movement sleep; and a weak association for duration of deep sleep. Basis B1 significantly overestimates total sleep time, sleep efficiency, and duration of light sleep and significantly underestimates wake after sleep onset and duration of deep sleep.ConclusionsBasis B1 demonstrated utility for estimates of gross sleep parameters and performed similarly to actigraphy for estimates of total sleep time. Basis B1 specificity was poor, and Basis B1 is not useful for the assessment of wake. Basis B1 accuracy for sleep stages was better than chance but is not a suitable replacement for PSG assessment. Despite low cost, ease of use, and attractiveness for patients, consumer devices are not yet accurate or reliable enough to guide treatment decision making in clinical settings.
| S-EPMC7849656 | biostudies-literature
Project description:BACKGROUND:The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns. OBJECTIVE:We aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach's output with that of an existing commercial wearable device's algorithms. METHODS:In this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual's data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified. RESULTS:When applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants. CONCLUSIONS:Our unsupervised approach can be effectively implemented based on an individual's multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters.
| S-EPMC7439146 | biostudies-literature
Project description:BackgroundActigraphs 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.MethodsSleep 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.ResultsBased 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.ConclusionsAn 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.
| S-EPMC7498244 | biostudies-literature
Project description:BackgroundThe rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG).Materials and methodsThis validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch.ResultsWe evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment.ConclusionThe results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.
| S-EPMC9584568 | biostudies-literature
Project description:Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea-hypopnea events per hour of sleep (apnea-hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7-40] h - 1 . The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up.
| S-EPMC6983183 | biostudies-literature
Project description:BackgroundAdolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group.ObjectiveThe goal of the research was to investigate the feasibility and acceptability of sleep EEG wearable devices among adolescents aged 11 to 17 years.MethodsA total of 104 adolescents aged 11 to 17 years participated in 7 days of at-home sleep recording using a self-administered wearable sleep EEG device (Zmachine Insight+, General Sleep Corporation) as well as a wristworn actigraph. Feasibility was assessed as the number of full nights of successful recording completed by adolescents, and acceptability was measured by the wearable acceptability survey for sleep. Feasibility and acceptability were assessed separately for the sleep EEG device and wristworn actigraph.ResultsA total of 94.2% (98/104) of adolescents successfully recorded at least 1 night of data using the sleep EEG device (mean number of nights 5.42; SD 1.71; median 6, mode 7). A total of 81.6% (84/103) rated the comfort of the device as falling in the comfortable to mildly uncomfortable range while awake. A total of 40.8% (42/103) reported typical sleep while using the device, while 39.8% (41/103) indicated minimal to mild device-related sleep disturbances. A minority (32/104, 30.8%) indicated changes in their sleep position due to device use, and very few (11/103, 10.7%) expressed dissatisfaction with their experience with the device. A similar pattern was observed for the wristworn actigraph device.ConclusionsWearable sleep EEG appears to represent a feasible, acceptable method for sleep assessment among adolescents and may have utility for assessing and treating sleep disturbances at a population level. Future studies with adolescents should evaluate strategies for further improving usability of such devices, assess relationships between sleep EEG-derived metrics and health outcomes, and investigate methods for incorporating data from these devices into emerging digital interventions and applications.Trial registrationClinicalTrials.gov NCT03843762; https://clinicaltrials.gov/ct2/show/NCT03843762.
| S-EPMC7563632 | biostudies-literature
Project description:Study objectiveThe objective of this study was to assess the accuracy of automatic diagnosis of obstructive sleep apnea (OSA) with a new, small, acoustic-based, wearable technology (AcuPebble SA100), by comparing it with standard type 1 polysomnography (PSG) diagnosis.Material and methodsThis observational, prospective study was carried out in a Spanish hospital sleep apnea center. Consecutive subjects who had been referred to the hospital following primary care suspicion of OSA were recruited and underwent in-laboratory attended PSG, together with the AcuPebble SA100 device simultaneously overnight from January to December 2022.ResultsA total of 80 patients were recruited for the trial. The patients had a median Epworth scoring of 10, a mean of 10.4, and a range of 0-24. The mean AHI obtained with PSG plus sleep clinician marking was 23.2, median 14.3 and range 0-108. The study demonstrated a diagnostic accuracy (based on AHI) of 95.24%, sensitivity of 92.86%, specificity of 97.14%, positive predictive value of 96.30%, negative predictive value of 94.44%, positive likelihood ratio of 32.50 and negative likelihood ratio of 0.07.ConclusionsThe AcuPebble SA100 (EU) device has demonstrated an accurate automated diagnosis of OSA in patients undergoing in-clinic sleep testing when compared against the gold-standard reference of in-clinic PSG.
| S-EPMC10816319 | biostudies-literature
Project description:BackgroundVariations in body temperature are highly informative during an illness. To date, there are not many adequate studies that have investigated the feasibility of a wearable wrist device for the continuous monitoring of body surface temperatures in humans.ObjectiveThe objective of this study was to validate the performance of HEARThermo, an innovative wearable device, which was developed to continuously monitor the body surface temperature in humans.MethodsWe implemented a multi-method research design in this study, which included 2 validation studies-one in the laboratory and one with human subjects. In validation study I, we evaluated the test-retest reliability of HEARThermo in the laboratory to measure the temperature and to correct the values recorded by each HEARThermo by using linear regression models. We conducted validation study II on human subjects who wore HEARThermo for the measurement of their body surface temperatures. Additionally, we compared the HEARThermo temperature recordings with those recorded by the infrared skin thermometer simultaneously. We used intraclass correlation coefficients (ICCs) and Bland-Altman plots to analyze the criterion validity and agreement between the 2 measurement tools.ResultsA total of 66 participants (age range, 10-77 years) were recruited, and 152,881 completed data were analyzed in this study. The 2 validation studies in the laboratory and on human skin indicated that HEARThermo showed a good test-retest reliability (ICC 0.96-0.98) and adequate criterion validity with the infrared skin thermometer at room temperatures of 20°C-27.9°C (ICC 0.72, P<.001). The corrected measurement bias averaged -0.02°C, which was calibrated using a water bath ranging in temperature from 16°C to 40°C. The values of each HEARThermo improved by the regression models were not significantly different from the temperature of the water bath (P=.19). Bland-Altman plots showed no visualized systematic bias. HEARThermo had a bias of 1.51°C with a 95% limit of agreement between -1.34°C and 4.35°C.ConclusionsThe findings of our study show the validation of HEARThermo for the continuous monitoring of body surface temperatures in humans.
| S-EPMC7904403 | biostudies-literature