Sleep Apnea Screening with a Contact:Free Under:the:Mattress Sensor

Davidovich, Maayan L. Yizraeli; Karasik, Roman; Tal, Asher; Shinar, Zvika

doi:10.22489/cinc.2016.246-183

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Table 7 shows, in the patient selection domain, 3 studies with a high risk of bias [ 29 , 47 , 50 ], 6 with a low risk [ 16 , 30 , 45 , 48 , 49 , 51 ], and 2 with unclear risk [ 46 , 52 ]. In turn, in the index test domain, 4 studies had a high risk [ 29 , 47 , 51 , 52 ], namely in questions Q5 and Q10, and the other 7 studies presented a low risk of bias [ 16 , 30 , 45 , 46 , 48 - 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…Bed or mattress-based sensors, for which the description can be found in Table 6, are also a big part of our pool of articles, with 11 (27%) of the 41 studies analyzing the performance and screening capability of these pressure-based sensing devices in the context of OSA [16,30,[45][46][47][48][49][50][51][52]. In addition to under-the-mattress sensors, there is also a smart pillow paired with an oximeter, which offers diagnostic capability by detecting apnea events and interventional intent by being able to inflate and change its conformation and deobstruct the patient's airway [29].…”

Section: Bed or Mattress Sensorsmentioning

confidence: 99%

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The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review

Duarte¹,

Rodrigues²,

Ferreira-Santos³

2023

J Med Internet Res

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Background Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard. Objective This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population. Methods We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures. Results We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat—a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events. Conclusions These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings. Trial Registration PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748

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Section: Resultsmentioning

confidence: 99%

Section: Bed or Mattress Sensorsmentioning

confidence: 99%

The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review

Duarte¹,

Rodrigues²,

Ferreira-Santos³

2023

J Med Internet Res

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“…An advantage of using a noncontact method over contact-based approaches is ease of use and convenience. Davidovich et al [ 23 ] used a piezo-electric sensor under a mattress to estimate the AHI. They obtained an R 2 value of 0.86 for AHI estimation, and accuracy and F1-score values of 88% and 84%, respectively, in identifying individuals with AHI≥15.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, using six cameras and the Kinect will be difficult to set up in clinical or home settings, which hinders large-scale adoption. Davidovich et al [ 23 ] developed a new framework to extract the breathing pattern from a piezo-electric sensor placed under the patient’s mattress through extracting time and frequency domain features and then calculating the AHI. Nandakumar et al [ 24 ] used a smartphone to emit inaudible waves and to analyze the waves’ echoes from the user’s body to detect respiratory events.…”

Section: Introductionmentioning

confidence: 99%

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

Akbarian¹,

Ghahjaverestan²,

Yadollahi³

et al. 2021

J Med Internet Res

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Background Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. Conclusions This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

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Current and future strategies for diagnostic and management of obstructive sleep apnea

Khurana

Soda

Shiddiky

et al. 2021

Expert Review of Molecular Diagnostics

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Exosomes are nano-sized extracellular vesicles that serve as a communications system between cells and has shown tremendous promise as liquid biopsy biomarkers in diagnostic, prognostic, and even therapeutic use in different human diseases. Due to the natural heterogeneity of exosomes, there is a need to separate exosomes into distinct subpopulations to enable full interrogation of exosome biology and function prior to the possibility of clinical translation. Currently, there exists a multitude of different exosome isolation and characterization approaches which can, in limited capacity, separate exosomes based on either biophysical or biochemical characteristics. While notable reviews in recent years have reviewed these approaches for bulk exosome sorting, we herein present an update of technological advancements and challenges towards isolation and characterization of distinct exosome subpopulations.

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Sleep Apnea Screening with a Contact:Free Under:the:Mattress Sensor

Abstract: Sleep apnea is a highly prevalent yet under-diagnosed condition. This study tested a novel algorithm for sleep apnea screening with a contact-free system based on a piezo-electric sensor (PE system -EarlySense Ltd)

Cited by 8 publications

References 6 publications

The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review

The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

Current and future strategies for diagnostic and management of obstructive sleep apnea

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