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Abu K, Khraiche ML, Amatoury J. Obstructive sleep apnea diagnosis and beyond using portable monitors. Sleep Med 2024; 113:260-274. [PMID: 38070375 DOI: 10.1016/j.sleep.2023.11.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/03/2023] [Accepted: 11/21/2023] [Indexed: 01/07/2024]
Abstract
Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.
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Affiliation(s)
- Kareem Abu
- Biomedical Engineering Program, Maroun Semaan Faculty of Engineering and Architecture (MSFEA), American University of Beirut, Beirut, Lebanon; Neural Engineering and Nanobiosensors Group, American University of Beirut, Beirut, Lebanon; Sleep and Upper Airway Research Group (SUARG), American University of Beirut, Beirut, Lebanon
| | - Massoud L Khraiche
- Biomedical Engineering Program, Maroun Semaan Faculty of Engineering and Architecture (MSFEA), American University of Beirut, Beirut, Lebanon; Neural Engineering and Nanobiosensors Group, American University of Beirut, Beirut, Lebanon
| | - Jason Amatoury
- Biomedical Engineering Program, Maroun Semaan Faculty of Engineering and Architecture (MSFEA), American University of Beirut, Beirut, Lebanon; Sleep and Upper Airway Research Group (SUARG), American University of Beirut, Beirut, Lebanon.
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Yin J, Xu J, Ren TL. Recent Progress in Long-Term Sleep Monitoring Technology. BIOSENSORS 2023; 13:395. [PMID: 36979607 PMCID: PMC10046225 DOI: 10.3390/bios13030395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.
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Affiliation(s)
- Jiaju Yin
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jiandong Xu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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Montazeri Ghahjaverestan N, Fan W, Aguiar C, Yu J, Bradley TD. Respiratory Motion and Airflow Estimation During Sleep Using Tracheal Movement and Sound. Nat Sci Sleep 2022; 14:1213-1223. [PMID: 35800029 PMCID: PMC9255718 DOI: 10.2147/nss.s360970] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/07/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Due to lack of access and high cost of polysomnography, portable sleep apnea testing has been developed to diagnose sleep apnea. Despite being less expensive, and having fewer sensors and reasonable accuracy in identifying sleep apnea, such devices can be less accurate than polysomnography in detecting apneas/hypopneas. To increase the accuracy of apnea/hypopnea detection, an accurate airflow estimation is required. However, current airflow measurement techniques employed in portable devices are inconvenient and subject to displacement during sleep. In this study, algorithms were developed to estimate respiratory motion and airflow using tracheo-sternal motion and tracheal sounds. PATIENTS AND METHODS Adults referred for polysomnography were included. Simultaneous to polysomnography, a patch device with an embedded 3-dimensional accelerometer and microphone was affixed to the suprasternal notch to record tracheo-sternal motion and tracheal sounds, respectively. Tracheo-sternal motion was used to train two mathematical models for estimating changes in respiratory motion and airflow compared to simultaneously measured thoracoabdominal motion and nasal pressure from polysomnography. The amplitude of the estimated airflow was then adjusted by the tracheal sound envelope in segments with unstable breathing. RESULTS Two hundred and fifty-two subjects participated in this study. Overall, the algorithms provided highly accurate estimates of changes in respiratory motion and airflow with mean square errors (MSE) of 3.58 ± 0.82% and 2.82 ± 0.71%, respectively, compared to polysomnographic signals. The estimated motion and airflow from the patch signals detected apneas and hypopneas scored on polysomnography in 63.9% and 88.3% of cases, respectively. CONCLUSION This study presents algorithms to accurately estimate changes in respiratory motion and airflow, which provides the ability to detect respiratory events during sleep. Our study suggests that such a simple and convenient method could be used for portable monitoring to detect sleep apnea. Further studies will be required to test this possibility.
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Affiliation(s)
- Nasim Montazeri Ghahjaverestan
- Sleep Research Laboratory of the University Health Network Toronto Rehabilitation Institute, Toronto, Ontario, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Wei Fan
- Bresotec Inc., Toronto, Ontario, Canada
| | | | | | - T Douglas Bradley
- Sleep Research Laboratory of the University Health Network Toronto Rehabilitation Institute, Toronto, Ontario, Canada.,Bresotec Inc., Toronto, Ontario, Canada.,Department of Medicine of the University of Toronto, Toronto, Ontario, Canada.,Department of Medicine of the University Health Network Toronto General Hospital, Toronto, Ontario, Canada
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Sadoughi A, Shamsollahi MB, Fatemizadeh E. Automatic detection of respiratory events during sleep from Polysomnography data using Layered Hidden Markov Model. Physiol Meas 2021; 43. [PMID: 34936995 DOI: 10.1088/1361-6579/ac45e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/22/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings. APPROACH In this study, a hierarchy of HMMs named Layered HMM was presented to detect respiratory events from PSG recordings. The recordings of 210 PSGs from Massachusetts General Hospital's database were used for this study. To develop detection algorithms, extracted feature signals from airflow, movements over the chest and abdomen, and oxygen saturation in blood (SaO2) were chosen as observations. The respiratory disturbance index (RDI) was estimated as the number of apneas, hypopneas, and RERAs per hour of sleep. MAIN RESULTS The best F1 score of the event by event detection algorithm was between 0.22±0.16 and 0.70±0.08 for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived RDI (R2=0.91, p<0.0001). The best recall of RERA detection was achieved 0.45±0.27. SIGNIFICANCE The results showed that the layered structure can improve the performance of the detection of respiratory events during sleep.
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Affiliation(s)
- Azadeh Sadoughi
- Department of Biomedical Engineering, Islamic Azad University Science and Research Branch, Science and Research Branch,shodada Hesarak blvd, Daneshgah Square,Sattari Highway,Tehran, I.R. IRAN;, Tehran, Tehran, 1477893855 , Iran (the Islamic Republic of)
| | - Mohammad Bagher Shamsollahi
- Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Sharif University of Technology, Azadi Ave, Tehran, Iran, Tehran, Tehran, 1458889694, Iran (the Islamic Republic of)
| | - Emad Fatemizadeh
- Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Sharif University of Technology, Azadi Ave, Tehran, Iran, Tehran, Tehran, 1458889694, Iran (the Islamic Republic of)
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Fu J, Teng WN, Li W, Chiou YW, Huang D, Liu J, Ting CK, Tsou MY, Yu L. Estimation of Respiratory Nasal Pressure and Flow Rate Signals Using Different Respiratory Sound Features. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Montazeri Ghahjaverestan N, Saha S, Kabir M, Gavrilovic B, Zhu K, Yadollahi A. Sleep apnea severity based on estimated tidal volume and snoring features from tracheal signals. J Sleep Res 2021; 31:e13490. [PMID: 34553793 DOI: 10.1111/jsr.13490] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/20/2021] [Accepted: 09/07/2021] [Indexed: 02/01/2023]
Abstract
Sleep apnea can be characterized by reductions in the respiratory tidal volume. Previous studies showed that the tidal volume can be estimated from tracheal sounds and movements called tracheal signals. Additionally, tracheal sounds include the sounds of snoring, a common symptom of obstructive sleep apnea. This study investigates the feasibility of estimating the severity of sleep apnea, as quantified by the apnea/hypopnea index (AHI), using the estimated tidal volume and snoring sounds extracted from tracheal signals. Tracheal signals were recorded simultaneously with polysomnography (PSG). The tidal volume was estimated from tracheal signals. The reductions in the tidal volume were detected as potential respiratory events. Additionally, features related to snoring sounds, which quantified variability, temporal clusters, and dominant frequency of snores, were extracted. A step-wise regression model and a greedy search algorithm were used sequentially to select the optimal set of features to estimate the apnea/hypopnea index and classify participants into healthy individuals and patients with sleep apnea. Sixty-one participants with suspected sleep apnea (age: 51 ± 16, body mass index: 29.5 ± 6.4 kg/m2 , apnea/hypopnea index: 20.2 ± 21.2 event/h) who were referred for a sleep test were recruited. The estimated apnea/hypopnea index was strongly correlated with the polysomnography-based apnea/hypopnea index (R2 = 0.76, p < 0.001). The accuracy of detecting sleep apnea for the apnea/hypopnea index cutoff of 15 events/h was 78.69% and 83.61% with and without using snore-related features. These findings suggest that acoustic estimation of airflow and snore-related features can provide a convenient and reliable method for screening of sleep apnea.
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Affiliation(s)
- Nasim Montazeri Ghahjaverestan
- KITE, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Shumit Saha
- KITE, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Muammar Kabir
- KITE, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Bojan Gavrilovic
- KITE, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Kaiyin Zhu
- KITE, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada
| | - Azadeh Yadollahi
- KITE, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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