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Khalil C, Zarabi S, Kirkham K, Soni V, Li Q, Huszti E, Yadollahi A, Taati B, Englesakis M, Singh M. Validity of non-contact methods for diagnosis of Obstructive Sleep Apnea: a systematic review and meta-analysis. J Clin Anesth 2023; 87:111087. [PMID: 36868010 DOI: 10.1016/j.jclinane.2023.111087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 01/16/2023] [Accepted: 02/20/2023] [Indexed: 03/05/2023]
Abstract
STUDY OBJECTIVE Obstructive Sleep Apnea (OSA) is associated with increased perioperative cardiac, respiratory and neurological complications. Pre-operative OSA risk assessment is currently done through screening questionnaires with high sensitivity but poor specificity. The objective of this study was to evaluate the validity and diagnostic accuracy of portable, non-contact devices in the diagnosis of OSA as compared with polysomnography. DESIGN This study is a systematic review of English observational cohort studies with meta-analysis and risk of bias assessment. SETTING Pre-operative, including in the hospital and clinic setting. PATIENTS Adult patients undergoing sleep apnea assessment using polysomnography and an experimental non-contact tool. INTERVENTIONS A novel non-contact device, which does not utilize any monitor that makes direct contact with the patient's body, in conjunction with polysomnography. MEASUREMENTS Primary outcomes included pooled sensitivity and specificity of the experimental device in the diagnosis of obstructive sleep apnea, in comparison to gold-standard polysomnography. RESULTS Twenty-eight of 4929 screened studies were included in the meta-analysis. A total of 2653 patients were included with the majority being patients referred to a sleep clinic (88.8%). Average age was 49.7(SD±6.1) years, female sex (31%), average body mass index of 29.5(SD±3.2) kg/m2, average apnea-hypopnea index (AHI) of 24.7(SD±5.6) events/h, and pooled OSA prevalence of 72%. Non-contact technology used was mainly video, sound, or bio-motion analysis. Pooled sensitivity and specificity of non-contact methods in moderate to severe OSA diagnosis (AHI > 15) was 0.871 (95% CI 0.841,0.896, I2 0%) and 0.8 (95% CI 0.719,0.862), respectively (AUC 0.902). Risk of bias assessment showed an overall low risk of bias across all domains except for applicability concerns (none were conducted in the perioperative setting). CONCLUSION Available data indicate contactless methods have high pooled sensitivity and specificity for OSA diagnosis with moderate to high level of evidence. Future research is needed to evaluate these tools in the perioperative setting.
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Affiliation(s)
- Carlos Khalil
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada
| | - Sahar Zarabi
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada
| | - Kyle Kirkham
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada; Department of Anesthesiology and Pain Medicine, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Vedish Soni
- McMaster University, 1280 Main Street West, Hamilton, ON, Canada, L8S 4L8
| | - Qixuan Li
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada; Biostatistics Research Unit, University Health Network; 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Ella Huszti
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada; Biostatistics Research Unit, University Health Network; 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Azadeh Yadollahi
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada; KITE-Toronto Rehabilitation Institute (TRI), University Health Network, 550 University Avenue, Toronto, ON M5G 2A2, Canada
| | - Babak Taati
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada; KITE-Toronto Rehabilitation Institute (TRI), University Health Network, 550 University Avenue, Toronto, ON M5G 2A2, Canada
| | - Marina Englesakis
- Library and Information Services, University Health Network, 200 Elizabeth St., Toronto, ON M5G 2C4, Canada
| | - Mandeep Singh
- University of Toronto, 27 King's College Cir, Toronto, ON M5S 1A1, Canada; Department of Anesthesiology and Pain Medicine, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada.
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2
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Huang Z, Zhou N, Chattrattrai T, van Selms MKA, de Vries R, Hilgevoord AAJ, de Vries N, Aarab G, Lobbezoo F. Associations between snoring and dental sleep conditions: A systematic review. J Oral Rehabil 2023; 50:416-428. [PMID: 36691754 DOI: 10.1111/joor.13422] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 11/06/2022] [Accepted: 01/13/2023] [Indexed: 01/25/2023]
Abstract
BACKGROUND It is important for dentists to know if the presence of snoring is associated with the presence of other dental sleep conditions (e.g. obstructive sleep apnea [OSA], sleep bruxism [SB], gastroesophageal reflux disease [GERD], xerostomia and oro-facial pain). If so, dentists could play a significant role in the early recognition and management of these conditions. OBJECTIVES This systematic review aimed to: (i) investigate the associations between the presence of snoring and the presence of other dental sleep conditions; and (ii) determine if it is clinically relevant that dentists assess snoring in their population. METHODS The literature search was performed in PubMed and Embase.com in collaboration with a medical librarian. Studies were eligible if they employed regression models to assess whether snoring was associated with other dental sleep conditions, and/or investigated the incidence of snoring in patients with other dental sleep conditions and vice versa. RESULTS Of the 5299 retrieved references, 36 eligible studies were included. The available evidence indicates that the presence of snoring is associated with higher probabilities of OSA, GERD and headache. Due to limited evidence and conflicting findings, the currently available articles are not indicative of associations between the presence of snoring and the presence of SB and oral dryness. CONCLUSION Within the limitations of this study, it can be concluded that the presence of snoring is associated with higher probabilities of OSA, GERD and headache. Therefore, it is clinically relevant that dentists assess snoring in their patient population.
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Affiliation(s)
- Zhengfei Huang
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology, OLVG, Amsterdam, The Netherlands
| | - Ning Zhou
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam UMC location University of Amsterdam, Department of Oral and Maxillofacial Surgery, University of Amsterdam, Amsterdam, The Netherlands.,Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Thiprawee Chattrattrai
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maurits K A van Selms
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ralph de Vries
- Medical Library, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Nico de Vries
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Otorhinolaryngology - Head and Neck Surgery, OLVG, Amsterdam, The Netherlands.,Department of Otorhinolaryngology - Head and Neck Surgery, Antwerp University Hospital (UZA), Antwerp, Belgium
| | - Ghizlane Aarab
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank Lobbezoo
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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3
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Portable evaluation of obstructive sleep apnea in adults: A systematic review. Sleep Med Rev 2023; 68:101743. [PMID: 36657366 DOI: 10.1016/j.smrv.2022.101743] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/10/2022] [Accepted: 12/23/2022] [Indexed: 01/07/2023]
Abstract
Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.
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4
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Wang B, Tang X, Ai H, Li Y, Xu W, Wang X, Han D. Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning. Nat Sci Sleep 2022; 14:2033-2045. [PMID: 36394068 PMCID: PMC9653035 DOI: 10.2147/nss.s373367] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE This study aimed to propose a novel deep-learning method for automatic sleep apneic event detection and thus to estimate the apnea hypopnea index (AHI) and identify obstructive sleep apnea (OSA) in an event-by-event manner solely based on sleep sounds obtained by a noncontact audio recorder. METHODS We conducted a cross-sectional study of participants with habitual snoring or heavy breathing sounds during sleep to train and test a deep convolutional neural network named OSAnet for the detection of OSA based on sleep sounds. Polysomnography (PSG) was conducted, and sleep sounds were recorded simultaneously in a regular room without noise attenuation. The study was conducted in two phases. In phase one, eligible participants were enrolled and randomly allocated into training and validation groups for deep learning algorithm development. In phase two, eligible patients were enrolled in a test group for algorithm assessment. Sensitivity, specificity, accuracy, unweighted Cohen kappa coefficient (κ) and the area under the curve (AUC) were calculated using PSG as the reference standard. RESULTS A total of 135 participants were randomly divided into a training group (n, 116) and a validation group (n, 19). An independent test group of 59 participants was subsequently enrolled. Our algorithm achieved a precision of 0.81 and sensitivity of 0.78 in the test group for overall sleep event detection. The algorithm exhibited robust diagnostic performance to identify severe cases with a sensitivity of 95.6% and specificity of 91.6%. CONCLUSION Our results showed that a deep learning algorithm based on sleep sounds recorded by a noncontact voice recorder served as a feasible tool for apneic event detection and OSA identification. This technique may hold promise for OSA assessment in the community in a relatively comfortable and low-cost manner. Further studies to develop a tool based on a home-based setting are warranted.
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Affiliation(s)
- Bochun Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People's Republic of China
| | - Xianwen Tang
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Hao Ai
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Yanru Li
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China.,Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Wen Xu
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China.,Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Xingjun Wang
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Demin Han
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China.,Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
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5
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Kim JW, Shin J, Lee K, Won TB, Rhee CS, Cho SW. Prediction of Oxygen Desaturation by Using Sound Data From a Noncontact Device: A Proof-of-Concept Study. Laryngoscope 2021; 132:901-905. [PMID: 34873695 DOI: 10.1002/lary.29971] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 11/04/2021] [Accepted: 11/24/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVES/HYPOTHESIS Prediction of the apnea-hypopnea index (AHI) from breathing sounds during sleep could be used to prescreen for obstructive sleep apnea (OSA). In addition, the oxygen desaturation index (ODI) is a known risk factor for developing cardiovascular disease in OSA patients. This study focused on estimation of ODI from a noncontact manner from sleep breathing sounds. STUDY DESIGN Retrospective study. METHODS Patients who visited the sleep center due to snoring or sleep apnea underwent polysomnography in lab overnight. Sound recordings were made during polysomnography using a microphone. After noise reduction, the sound data were segmented into 5 seconds windows and features were extracted. Binary classification and regression analyses were performed to estimate the ODI during sleep (model 1). This was re-tested after inclusion of body mass index (BMI) and age as additional features (model 2: BMI only, model 3: BMI and age). RESULTS We included 116 patients. The mean age and AHI of all patients were 50.4 ± 16.7 years and 23.0 ± 24.0 events/hr. In binary classification, for ODI cutoff values of 5, 15, and 30 events/hr, the areas under the curve were 0.88, 0.93, 0.91, respectively, and accuracies were 85.34, 86.21, and 87.07, respectively. In regression analysis, the correlation coefficient and mean absolute error were 0.80 and 9.60 events/hr, respectively. In models 2 and 3, the correlation coefficient and mean absolute error were 0.82, 9.44 events/hr and 0.81, 9.6 events/hr, respectively. CONCLUSION Prediction of ODI from sleep sound seems to be feasible. Additional clinical feature such as BMI may increase overall predictability. LEVEL OF EVIDENCE IV Laryngoscope, 2021.
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Affiliation(s)
- Jeong-Whun Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.,Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul National University Medical Research Center, Seoul, Korea
| | - Jaeyoung Shin
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, South Korea
| | - Kyogu Lee
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, South Korea
| | - Tae-Bin Won
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Chae-Seo Rhee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.,Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul National University Medical Research Center, Seoul, Korea
| | - Sung-Woo Cho
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
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6
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Hou L, Pan Q, Yi H, Shi D, Shi X, Yin S. Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds. Front Digit Health 2021; 2:613725. [PMID: 34713075 PMCID: PMC8522026 DOI: 10.3389/fdgth.2020.613725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 12/18/2020] [Indexed: 11/13/2022] Open
Abstract
This paper proposes a new perspective of analyzing non-linear acoustic characteristics of the snore sounds. According to the ERB (Equivalent Rectangular Bandwidth) scale used in psychoacoustics, the ERB correlation dimension (ECD) of the snore sound was computed to feature different severity levels of sleep apnea hypopnea syndrome (SAHS). For the training group of 93 subjects, snore episodes were manually segmented and the ECD parameters of the snores were extracted, which established the gaussian mixture models (GMM). The nocturnal snore sound of the testing group of another 120 subjects was tested to detect SAHS snores, thus estimating the apnea hypopnea index (AHI), which is called AHIECD. Compared to the AHIPSG value of the gold standard polysomnography (PSG) diagnosis, the estimated AHIECD achieved an accuracy of 87.5% in diagnosis the SAHS severity levels. The results suggest that the ECD vectors can be effective parameters for screening SAHS.
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Affiliation(s)
- Limin Hou
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Qiang Pan
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Hongliang Yi
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Dan Shi
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xiaoyu Shi
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Shankai Yin
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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7
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Kim JW, Kim T, Shin J, Lee K, Choi S, Cho SW. Prediction of Apnea-Hypopnea Index Using Sound Data Collected by a Noncontact Device. Otolaryngol Head Neck Surg 2020; 162:392-399. [PMID: 32013710 DOI: 10.1177/0194599819900014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To predict the apnea-hypopnea index (AHI) in patients with obstructive sleep apnea (OSA) using data from breathing sounds recorded using a noncontact device during sleep. STUDY DESIGN Prospective cohort study. SETTING Tertiary referral hospital. SUBJECT AND METHODS Audio recordings during sleep were performed using an air-conduction microphone during polysomnography. Breathing sounds recorded from all sleep stages were analyzed. After noise reduction preprocessing, the audio data were segmented into 5-second windows and sound features were extracted. Estimation of AHI by regression analysis was performed using a Gaussian process, support vector machine, random forest, and simple linear regression, along with 10-fold cross-validation. RESULTS In total, 116 patients who underwent attended, in-laboratory, full-night polysomnography were included. Overall, random forest resulted in the highest performance with the highest correlation coefficient (0.83) and least mean absolute error (9.64 events/h) and root mean squared error (13.72 events/h). Other models resulted in somewhat lower but similar performances, with correlation coefficients ranging from 0.74 to 0.79. The estimated AHI tended to be underestimated as the severity of OSA increased. Regarding bias and precision, estimation performances in the severe OSA subgroup were the lowest, regardless of the model used. Among sound features, derivative of the area methods of moments of overall standard deviation demonstrated the highest correlation with AHI. CONCLUSION AHI was fairly predictable by using data from breathing sounds generated during sleep. The prediction model may be useful not only for prescreening but also for follow-up after treatment in patients with OSA.
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Affiliation(s)
- Jeong-Whun Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Korea
| | - Taehoon Kim
- Mobile Communications Business, Samsung Electronics, Suwon, Korea
| | - Jaeyoung Shin
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| | - Kyogu Lee
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| | - Sunkyu Choi
- Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sung-Woo Cho
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Korea
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8
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Tal A, Shinar Z, Shaki D, Codish S, Goldbart A. Validation of Contact-Free Sleep Monitoring Device with Comparison to Polysomnography. J Clin Sleep Med 2017; 13:517-522. [PMID: 27998378 DOI: 10.5664/jcsm.6514] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 11/15/2016] [Indexed: 01/17/2023]
Abstract
STUDY OBJECTIVES To validate a contact-free system designed to achieve maximal comfort during long-term sleep monitoring, together with high monitoring accuracy. METHODS We used a contact-free monitoring system (EarlySense, Ltd., Israel), comprising an under-the-mattress piezoelectric sensor and a smartphone application, to collect vital signs and analyze sleep. Heart rate (HR), respiratory rate (RR), body movement, and calculated sleep-related parameters from the EarlySense (ES) sensor were compared to data simultaneously generated by the gold standard, polysomnography (PSG). Subjects in the sleep laboratory underwent overnight technician-attended full PSG, whereas subjects at home were recorded for 1 to 3 nights with portable partial PSG devices. Data were compared epoch by epoch. RESULTS A total of 63 subjects (85 nights) were recorded under a variety of sleep conditions. Compared to PSG, the contact-free system showed similar values for average total sleep time (TST), % wake, % rapid eye movement, and % non-rapid eye movement sleep, with 96.1% and 93.3% accuracy of continuous measurement of HR and RR, respectively. We found a linear correlation between TST measured by the sensor and TST determined by PSG, with a coefficient of 0.98 (R = 0.87). Epoch-by-epoch comparison with PSG in the sleep laboratory setting revealed that the system showed sleep detection sensitivity, specificity, and accuracy of 92.5%, 80.4%, and 90.5%, respectively. CONCLUSIONS TST estimates with the contact-free sleep monitoring system were closely correlated with the gold-standard reference. This system shows good sleep staging capability with improved performance over accelerometer-based apps, and collects additional physiological information on heart rate and respiratory rate.
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Affiliation(s)
- Asher Tal
- Soroka Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev
| | | | - David Shaki
- Soroka Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev
| | | | - Aviv Goldbart
- Soroka Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev
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9
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Meng L, Xu H, Guan J, Yi H, Wu H, Yin S. Validation of a novel sleep-monitoring system for diagnosing obstructive sleep apnea: A comparison with polysomnography. Exp Ther Med 2016; 12:2937-2941. [PMID: 27882098 PMCID: PMC5103728 DOI: 10.3892/etm.2016.3721] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Accepted: 03/21/2016] [Indexed: 02/07/2023] Open
Abstract
Overnight polysomnography (PSG) is currently the gold standard for diagnosing obstructive sleep apnea (OSA); however, it is time-consuming, expensive and uncomfortable for the patient. A micromovement sensitive mattress (MSM) sleep-monitoring system was developed as an alternative to PSG, however, there has yet to be a study verifying the accuracy of diagnosing OSA with this device. Therefore, the present study assessed the validity of the MSM sleep-monitoring system. Chinese Han participants who were suspected of having OSA were recruited between June 2013 and June 2014. The MSM sleep-monitoring system and PSG were utilized simultaneously overnight on each subject. The apnea-hypopnea index (AHI) was measured by the MSM sleep-monitoring system (AHIMSM) and compared with that determined by PSG (AHIPSG), revealing a significant correlation between the two values (r=0.97, P<0.001). Bland-Altman plots also indicated good agreement (97%) between MSM and PSG. Using an AHIPSG cut-off of ≥5, ≥15 and ≥30 events/h, the sensitivity (specificity) of detecting an AHIMSM of ≥5, ≥15, and ≥30 events/h were 94.9 (100%), 89.9 (96.9%) and 90.3% (94.9%), respectively. The areas under the receiver operating characteristic curve, which were used to differentiate an AHIPSG of ≥5, ≥15 and ≥30 events/h in clinically diagnosed OSA, were 0.984, 0.982 and 0.980, respectively. Thus, the MSM sleeping system may accurately diagnose OSA in the Chinese Han population. Further community-based studies with larger sample sizes are warranted to confirm the validity of this MSM sleeping system.
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Affiliation(s)
- Lili Meng
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, P.R. China
| | - Huajun Xu
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, P.R. China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, P.R. China
| | - Jian Guan
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, P.R. China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, P.R. China
| | - Hongliang Yi
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, P.R. China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, P.R. China
| | - Hongmin Wu
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, P.R. China
| | - Shankai Yin
- Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, P.R. China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, P.R. China
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10
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Lee LA, Lo YL, Yu JF, Lee GS, Ni YL, Chen NH, Fang TJ, Huang CG, Cheng WN, Li HY. Snoring Sounds Predict Obstruction Sites and Surgical Response in Patients with Obstructive Sleep Apnea Hypopnea Syndrome. Sci Rep 2016; 6:30629. [PMID: 27471038 PMCID: PMC4965759 DOI: 10.1038/srep30629] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 07/06/2016] [Indexed: 11/09/2022] Open
Abstract
Snoring sounds generated by different vibrators of the upper airway may be useful indicators of obstruction sites in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). This study aimed to investigate associations between snoring sounds, obstruction sites, and surgical responses (≥50% reduction in the apnea-hypopnea index [AHI] and <10 events/hour) in patients with OSAHS. This prospective cohort study recruited 36 OSAHS patients for 6-hour snoring sound recordings during in-lab full-night polysomnography, drug-induced sleep endoscopy (DISE), and relocation pharyngoplasty. All patients received follow-up polysomnography after 6 months. Fifteen (42%) patients with at least two complete obstruction sites defined by DISE were significantly, positively associated with maximal snoring sound intensity (40-300 Hz; odds ratio [OR], 1.25, 95% confidence interval [CI] 1.05-1.49) and body mass index (OR, 1.48, 95% CI 1.02-2.15) after logistic regression analysis. Tonsil obstruction was significantly, inversely correlated with mean snoring sound intensity (301-850 Hz; OR, 0.84, 95% CI 0.74-0.96). Moreover, baseline tonsil obstruction detected by either DISE or mean snoring sound intensity (301-850 Hz), and AHI could significantly predict the surgical response. Our findings suggest that snoring sound detection may be helpful in determining obstruction sites and predict surgical responses.
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Affiliation(s)
- Li-Ang Lee
- Department of Otorhinolaryngology - Head and Neck Surgery, Sleep Center, Linkou-Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, ROC.,Faculty of Medicine, College of Medicine, Chang Gung University, Taoyuan 33303, Taiwan, ROC
| | - Yu-Lun Lo
- Faculty of Medicine, College of Medicine, Chang Gung University, Taoyuan 33303, Taiwan, ROC.,Department of Thoracic Medicine, Sleep Center, Linkou-Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, ROC
| | - Jen-Fang Yu
- Graduate Institute of Medical Mechatronics, Taiouan Interdisciplinary Otolaryngology Laboratory, Chang Gung University, Taoyuan 33303, Taiwan
| | - Gui-She Lee
- Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei 11221, Taiwan, ROC.,Department of Otolaryngology, Taipei City Hospital, Ren-Ai Branch, Taipei 10629, Taiwan, ROC
| | - Yung-Lun Ni
- Department of Thoracic Medicine, Sleep Center, Linkou-Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, ROC.,Department of Chest Medicine, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung 42743, Taiwan, ROC
| | - Ning-Hung Chen
- Faculty of Medicine, College of Medicine, Chang Gung University, Taoyuan 33303, Taiwan, ROC.,Department of Thoracic Medicine, Sleep Center, Linkou-Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, ROC
| | - Tuan-Jen Fang
- Department of Otorhinolaryngology - Head and Neck Surgery, Sleep Center, Linkou-Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, ROC.,Faculty of Medicine, College of Medicine, Chang Gung University, Taoyuan 33303, Taiwan, ROC
| | - Chung-Guei Huang
- Department of Laboratory Medicine, Linkou-Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, ROC.,Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Tao-Yuan 33303, Taiwan, ROC.,Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Tao-Yuan 33303, Taiwan, ROC
| | - Wen-Nuan Cheng
- Department of Sports Sciences, University of Taipei, Tai-Pei 11153, Taiwan, ROC
| | - Hsueh-Yu Li
- Department of Otorhinolaryngology - Head and Neck Surgery, Sleep Center, Linkou-Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan, ROC.,Faculty of Medicine, College of Medicine, Chang Gung University, Taoyuan 33303, Taiwan, ROC.,Department of Sleep Medicine, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK
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