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Physician's Name. Subscribe Sign in. Table 1 wakefulnese shows the anthropometric information for the two groups of training and testing. Pathophysiology Etiology Evaluation Treatment Key Points.

Sleep apnea wakefulness -

Multiple questions or a separate roommate questionnaire do not greatly improve predictive ability, but gender and BMI information do. Another study to evaluate the performance of the STOP-Bang questionnaire as a screening tool for OSA was conducted [ 19 ].

The analysis concluded by demonstrating that the likelihood of having moderate-to-severe OSA increases with increasing the STOP-Bang score. A study to evaluate the STOP-Bang questionnaire was done [ 88 ]; the authors used a dataset of subjects attending a sleep clinic for PSG which was used to evaluate the performance of the questionnaire.

The outcome of the study in the test set was A cross-sectional study to compare different questionnaires for screening of OSA was conducted [ 18 ], the study included patients attending the sleep clinic for overnight PSG, and then the patients were administered four sleep questionnaires Berlin, Epworth Sleepiness Scale [ESS], STOP, and STOP-Bang.

The results showed that the STOP-Bang has the highest sensitivity among all OSA severity categories Finally, the Berlin, STOP, and STOP-Bang questionnaire sensitivity was quite high, but because of their low specificity, they produced more false-positive results and failed to exclude those who were at low risk.

Moreover, to determine which patients are at risk and to determine the ideal combination of these tools, the clinical utility of five different questionnaires—STOP, STOP-Bang, Berlin questionnaire, Epworth Sleepiness Scale, and 4-Variable Screening Tool—in a sleep clinic is being evaluated [ 90 ].

Like in the previous studies, the outcome shows that the highest specificity was found in 4-V, while SB had the highest sensitivity and AUC.

Their predictive value was not increased by combining various surveys. Yet, another more recent study compared the reliability of different questionnaires in the detection of OSA on subjects with different OSA severity, where the subjects completed five different types of questionnaires: the ESS questionnaire, the STOP-Bang questionnaire, the STOP questionnaire, the BQ questionnaire and the Pittsburgh Sleep Quality Index PSQI [ 91 ].

Moreover, the subjects were examined using limited PSG, and the performance of the questionnaires was evaluated. The results showed the highest sensitivity was achieved by STOP-Bang For specificity, the highest values were achieved by ESS Based on the results, the STOP-Bang and Berlin questionnaires were found to be the most reliable screening tool.

Also, the STOP questionnaire was found to have the most time-saving nature as is a short questionnaire. Table 8 presents a summary of all investigated papers in this section. Many studies have evaluated the performance of common OSA screening questionnaires like the ESS, Berlin questionnaire, STOP-Bang, and STOP questionnaire, revealing variations in accuracy and false-negative rates.

While some studies highlighted specific predictors within questionnaires, others emphasized potential redundancies. Comparative evaluations consistently showed high sensitivity but very low specificity, leading to increased false positives and the inability to exclude low-risk individuals.

Recent research identified STOP-Bang and Berlin questionnaires as having the highest sensitivity, while ESS exhibited the highest specificity. Despite their sensitivity, ongoing research and refinement are essential to address specificity limitations and optimize the clinical utility of OSA screening tools.

The sample size is an important part of any study; for OSA detection, this is one of the major issues since it is hard to collect other data from patients especially before and after the PSG recording [ 92 ].

Even though, recording PSG is hard to record since it is costly and requires the patient to sleep at the hospital sleep lab [ 14 ]. Most of the studies are performed on a relatively small sample size, and in many cases, the recorded datasets are imbalanced which causes biasing on the results towards one of the classes.

Based on that, any future studies must include larger datasets. The cost of the process to diagnose patients with OSA is high when it is referred to PSG, imaging studies, and NEP [ 29 , 37 , 43 ]. Another process like detection using breathing sounds and speech sounds is very cheap [ 73 ].

Moreover, researchers in their future research methods should ensure that their proposed systems are affordable. The effective parameters on the affordability consist of the technology of the process, fabrication materials, required equipment, and the transmission technology if required.

Usually, OSA diagnosis devices are not easy to use and require a complex setup of the process and a specialized person for the interpretation of the acquired data [ 43 ].

This is a major challenge to the current methodologies; based on that, researchers must take into consideration the simplicity of the process setup and in it is best cases the patient can set it up on their side with easy instructions.

The portability of any proposed system for wakefulness detection is OSA which is a main challenge, since most of the reviewed systems and methods required high computational processes when using data processing [ 93 ].

Moreover, some techniques like medical imaging require large equipment for data acquisition [ 28 ]. Based on that, researchers in the future must be able to use cloud computing and wireless transmission of data to overcome such challenges.

Measuring time is one of the issues during studies and affects the number of the included subjects in the studies [ 40 ]. Moreover, it also affects the ability to apply any system in real time [ 53 ]. Also, since our main focus in this review is on the detection of OSA during wakefulness, it is important and challenging to make proposed methods to record required measurements in a short time and even generate the results in a short time [ 93 ].

Any future research should focus on the required time for measuring as one of the main challenges to be overcome. Most of the proposed methods are focused on detecting if there is OSA or not based on a threshold applied to the recorded AHI [ 94 ]. One of the challenging things in the future development of OSA detection during wakefulness is to provide a system that can detect the severity of the OSA based on the predefined AHI value thresholds [ 45 ].

Such a system can help provide a full diagnosis system instead of a classification system. Developing an advanced and high-performance method that is able to detect OSA with very high performance is the ultimate goal of any detection and classification system [ 42 ].

Additionally, based on the previously discussed challenges, this can be quite challenging based on the number of subjects included in the studies [ 82 ]. So, the performance of the detection system also remains an important challenge by focusing on the specificity and sensitivity not only the accuracy and needs to be investigated further in-depth in any future work.

However, other severity parameters like total arousal index and SpO2 are very important to provide a full diagnosis of the patient and decide on a treatment option [ 67 ]. PSG assessments and home sleep tests measure these parameters, but most wakefulness techniques are unable to estimate or predict these parameters; there has been only one study [ 67 ].

In future methods, there is a need to provide a system able to estimate or predict these parameters and to be investigated further in-depth in any future work. False positives can emerge in various OSA detection techniques; for each of these techniques, there are different reasons behind false positives, and different guidance on managing an excessive number of clinically irrelevant OSA detections is needed.

These insights are essential for researchers, clinicians, and technologists striving to enhance the accuracy and reliability of OSA diagnosis during wakefulness [ 95 ]. By addressing the issue of false positives systematically across different OSA detection techniques, we aim to contribute to the development of more precise and clinically relevant methods [ 96 ].

The goal is to ensure that patients receive accurate diagnoses, appropriate treatment plans, and peace of mind, while healthcare resources are utilized efficiently and effectively. In the following, we will delve into specific techniques and their respective strategies for managing false positives [ 97 ].

For the use of imaging techniques, mitigating clinically irrelevant OSA detections involves implementing robust post-processing methods and automatically identifying and excluding artifacts [ 96 ].

It is crucial to set specific parameters during image acquisition and establish criteria for extracting anatomical features based on validated clinical data to distinguish between relevant and irrelevant findings.

Regular calibration of imaging equipment, adherence to standardized protocols, and employing standard device setups are essential to minimize false positives [ 95 , 97 ]. For various OSA detection methods, managing excessive clinically irrelevant detections necessitates specific strategies.

In NEP tests, clear clinical guidelines defining thresholds for collapsibility and guiding repeat tests, or different interpretations are crucial [ 95 ]. Training healthcare professionals in NEP interpretation nuances can further reduce the likelihood of excessive irrelevant detections [ 97 ].

In facial landmarks analysis, refining algorithms and incorporating machine learning models based on large datasets enhance landmark detection accuracy [ 98 ]. Similar precision improvements can be achieved in pharyngometry by establishing normative data for airway dimensions, considering dynamic changes during sleep, and comparing patient data to norms [ 95 , 97 ].

Advanced signal processing in breathing sound analysis, including using balanced groups dataset, appropriate recording protocols, and patient-specific characteristics, enhances accuracy [ 96 , 99 , ]. Similarly, in speech signal analysis, focusing on specific speech features, considering contextual information, and employing continuous monitoring and real-time feedback systems contribute to accuracy [ 98 , 99 ].

In questionnaires, refining designs, implementing scoring thresholds, and combining questionnaire data with physiological parameters improve diagnostic accuracy and reduce irrelevant detections [ 96 , 99 , ]. Overall, integrating these tailored strategies into each OSA detection technique enhances precision, reliability, and clinical relevance [ 96 ].

Gold standard OSA diagnosis, an overnight PSG sleep study, has many drawbacks such as being labor-intensive, time-consuming, expensive, and lack of availability in remote areas. Thus, research interest in detecting OSA during wakefulness within a few minutes has been on the rise, especially in the last decade.

This review has been dedicated to reviewing the studies dedicated to understanding OSA manifestation on the upper airway as well as technologies to screen and detect OSA during wakefulness.

This review has presented 57 journal papers and conference papers; all papers related to screening children were excluded since children, and adults have significant disparities in sleep and respiratory physiology and their OSA pathology [ ].

Having analyzed and condensed available literature, characteristics of a good OSA screening tool have been identified as 1 affordability, 2 ease of use, 3 portability, 4 executability during wakefulness, 5 prompt setup and measurement time, 6 large sample size testing, 7 non-invasiveness, 8 ability to screen for different OSA severity groups, 9 accuracy with high sensitivity and specificity, and 10 ability to provide physiological interpretation and information beyond AHI.

Most of these characteristics are a challenge that faces the past and current development of OSA wakefulness technologies. Given these characteristics, imaging techniques would not meet the design specifications for a future OSA screening tool, as imaging methods remain bulky, expensive, and not readily available outside of a clinical setting.

However, imaging techniques remain very helpful research tools to better understand the pathogenes of the disorder. Of the 57 reviewed papers, 40 papers proposed a classification analysis methodology, while only 12 papers of these 40 introduced only training results, 11 papers introduced validation results, and 25 papers introduced testing results.

Moreover, the number of participants per study was between 14 [ 36 ] and [ 80 ] individuals, and this number is still small given the heterogeneity of the OSA population and its confounding variables and also compared to the number of samples that application of artificial intelligence and deep learning required to achieve reliable results.

A major drawback with imaging techniques [ 28 , 29 , 30 , 36 , 37 , 58 ], negative expiratory pressure [ 41 , 43 , 44 , 45 ], and pyranometer-based studies [ 59 , 60 , 62 ] is that they did not introduce any testing classification results; these studies require further investigation with validation and blind testing results.

On the other hand, facial-related papers provided testing classification accuracies, but they were relatively low: they were between These results show that facial imaging still needs more development and may require combining extracted features from these techniques with other features such as anthropometric features to enhance the overall performance.

In contrast to the above studies, the OSA detection performance was increased in breathing sound—related papers, with testing classification accuracies between While tracheal breathing sound analysis has shown reasonably high blind test sensitivity and specificity, studies have shown the accuracy can still be benefited by combining some anthropometric features with the sound analysis [ 70 ].

Similar to breathing sounds, speech sound analysis can also be used for OSA detection. More variation was noticed in speech signal—related papers, with testing classification accuracies between 71 [ 80 ] and On the other hand, the greatest variation was seen in papers related to questionnaires, showing testing sensitivities between The oral cavity and clinical measurements related article provided However, the oral cavity and clinical measurement model has certain limitations that affect its accuracy and further development.

Air pressure—related papers provided However, the air pressure research paper was done on very small datasets; thus, further investigation and standardizing the instrumentation are required to confirm the robustness of the proposed methodology.

Furthermore, there is interest in predicting other OSA-related parameters that a PSG overnight measures, by breathing sound analysis during wakefulness [ 67 ].

Overall, combining different methodologies for wider reporting metrics, in addition to improved accuracy, may provide a more well-rounded, comprehensive screening tool for future use.

Non-invasive detection during wakefulness of OSA is important as it can resolve many current major issues such as long waiting time to have an overnight PSG and lack of OSA diagnosis by reducing the need for PSG assessment through a quick and accurate screening during wakefulness, thus, significantly reducing the economic burden of OSA on healthcare.

In addition, a reliable, comprehensive OSA detection tool would reduce possible perioperative morbidity and mortality, as well as facilitate faster treatment. There exist many studies that have investigated OSA screening during wakefulness, and yet, as suggested throughout the present review, opportunities for improvement exist to provide a measure for severity rather than only screening for OSA and non-OSA populations.

In this paper, different techniques for OSA detection during wakefulness are divided based on the main used methodology like imaging techniques, negative expiratory pressure, facial image landmarks, pharyngometry, breathing sound analysis, speech signal analysis, and questionnaires.

For each technique, all related papers are reviewed and summarized to show the main outcome. This review also highlights the road map for the design specifications which are required or preferred in any feature methodology for the wakefulness technique of OSA detection.

The future open path for research in this area will be the design of more comfortable, reliable, and accurate devices to provide comfortable, cost-effective, and accurate ways for wakefulness detection of OSA and its severity; these will reduce the need for PSG recordings, especially for the initial screening.

In a nutshell, this review shows that there is an increased focus by researchers on developing techniques for OSA detection during wakefulness.

Although there are promising results from surveyed papers, there is a need for more clinical validation of these methods on larger populations. Colten HR, Altevogt BM Sleep disorders and sleep deprivation: an unmet public health problem.

The national academies press, Washington, D. Kushida CA, Littner MR, Morgenthaler T et al Practice parameters for the indications for polysomnography and related procedures: an update for Sleep — Article PubMed Google Scholar.

American Academy of Sleep Medicine, Westchester, IL. Berry RB, Brooks R, Gamaldo CE et al The AASM manual for the scoring of sleep and associated events.

Rules Terminology Techn Specifications Darien Illinois Am Acad Sleep Med Google Scholar. Bradley TD, Floras JS Sleep apnea: implications in cardiovascular and cerebrovascular disease, 2nd edn. CRC Press, Boca Raton. Butt M, Dwivedi G, Khair O, Lip GYH Obstructive sleep apnea and cardiovascular disease.

Int J Cardiol — Espiritu JRD Health consequences of obstructive sleep apnea. In: Management of obstructive sleep apnea. Springer International Publishing, Cham, pp 23— Yoshihisa A, Takeishi Y Sleep disordered breathing and cardiovascular diseases. J Atheroscler Thromb — Article PubMed PubMed Central CAS Google Scholar.

Noda A, Yasuma F, Miyata S et al Sleep fragmentation and risk of automobile accidents in patients with obstructive sleep apnea—sleep fragmentation and automobile accidents in OSA. Health N Hav — Young T, Finn L, Peppard PE et al Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin Sleep Cohort.

Sleep Article PubMed PubMed Central Google Scholar. American Academy of Sleep Medicine Hidden health crisis costing america billions: underdiagnosing and undertreating obstructive sleep apnea draining healthcare system.

The Harvard Medical School Division of Sleep Medicine The price of fatigue: the surprising economic costs of unmanaged sleep apnea.

Harvard Medical School Division of Sleep Medicine Boston, Boston, MA. Tregear S, Reston J, Schoelles K, Phillips B Obstructive sleep apnea and risk of motor vehicle crash: systematic review and meta-analysis.

J Clin Sleep Med Stewart SA, Skomro R, Reid J et al Improvement in obstructive sleep apnea diagnosis and management wait times: a retrospective analysis of a home management pathway for obstructive sleep apnea. Can Respir J — Yadollahi A, Moussavi Z Acoustic obstructive sleep apnea detection.

In: Annual international conference of the ieee engineering in medicine and biology society. IEEE, pp — Chen L, Pivetta B, Nagappa M et al Validation of the STOP-Bang questionnaire for screening of obstructive sleep apnea in the general population and commercial drivers: a systematic review and meta-analysis.

Sleep Breath — Mazzotti DR, Keenan BT, Thorarinsdottir EH et al Is the Epworth Sleepiness Scale sufficient to identify the excessively sleepy subtype of OSA?

Chest — El-Sayed IH Comparison of four sleep questionnaires for screening obstructive sleep apnea. Egypt J Chest Dis Tuberc — Article Google Scholar. Nagappa M, Liao P, Wong J et al Validation of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnea among different populations: a systematic review and meta-analysis.

PLoS One e American Society of Anesthesiologists Practice guidelines for the perioperative management of patients with obstructive sleep apnea: An updated report. Anesthesiology — Finkelstein Y, Wolf L, Nachmani A et al Velopharyngeal anatomy in patients with obstructive sleep apnea versus normal subjects.

J Oral Maxillofac Surg — Lan Z, Itoi A, Takashima M et al Difference of pharyngeal morphology and mechanical property between OSAHS patients and normal subjects. Auris Nasus Larynx — Younes M Role of respiratory control mechanisms in the pathogenesis of obstructive sleep disorders.

J Appl Physiol — Article PubMed CAS Google Scholar. Malhotra A, Pillar G, Fogel R et al Upper-airway collapsibility: measurements and sleep effects. Ryan CM Bradley TD Pathogenesis of obstructive sleep apnea. Betts JG, DeSaix P, Johnson E et al Anatomy and physiology, 2nd ed.

OpenStax, Houston, TX. Elwali A, Moussavi Z Obstructive sleep apnea screening and airway structure characterization during wakefulness using tracheal breathing sounds. Ann Biomed Eng. Riley R, Guilleminault C, Herran J, Powell N Cephalometric analyses and flow-volume loops in obstructive sleep apnea patients.

Haponik EF, Smith PL, Bohlman ME et al Computerized tomography in obstructive sleep apnea. Correlation of airway size with physiology during sleep and wakefulness. Am Rev Respir Dis — Schwab RJ, Gefter WB, Hoffman EA et al Dynamic upper airway imaging during awake respiration in normal subjects and patients with sleep disordered breathing.

Ciscar MA, Juan G, Martõ Ânez V et al Magnetic resonance imaging of the pharynx in OSA patients and healthy subjects. Eur Respir J — Molnár V, Molnár A, Lakner Z et al The prognostic role of ultrasound and magnetic resonance imaging in obstructive sleep apnoea based on lateral oropharyngeal wall obstruction.

Bindi I, Ori M, Marchegiani M et al Diagnosis of upper airways collapse in moderate-to-severe OSAHS patients: a comparison between drug-induced sleep endoscopy and the awake examination. Eur Arch Otorhinolaryngol — Molnár V, Lakner Z, Molnár A et al Ultrasound and magnetic resonance imaging of the tongue in obstructive sleep apnoea.

Appl Sci Article CAS Google Scholar. Molnár V, Lakner Z, Molnár A et al The predictive role of subcutaneous adipose tissue in the pathogenesis of obstructive sleep apnoea. Life Darquenne C, Elliott AR, Sibille B et al Upper airway dynamic imaging during tidal breathing in awake and asleep subjects with obstructive sleep apnea and healthy controls.

Physiol Rep —9. Schwab RJ, Pasirstein M, Pierson R et al Identification of upper airway anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging.

Am J Respir Crit Care Med — Bland JM, Altman DG The odds ratio. BMJ Kim TY, Son J, Kim KG The recent progress in quantitative medical image analysis for computer aided diagnosis systems. Healthc Inform Res Lahav Y, Rosenzweig E, Heyman Z et al Tongue base ultrasound: a diagnostic tool for predicting obstructive sleep apnea.

Ann Otol Rhinol Laryngol — Tamisier R, Wuyam B, Nicolle I et al Awake flow limitation with negative expiratory pressure in sleep disordered breathing.

Sleep Med — JAMA — Carrera HL, Marcus CL, McDonough JM et al Negative expiratory pressure technique: an awake test to measure upper airway collapsibility in adolescents. Romano S, Salvaggio A, Hirata RP et al Upper airway collapsibility evaluated by a negative expiratory pressure test in severe obstructive sleep apnea.

Clinics — Romano S, Salvaggio A, Lo BA et al A negative expiratory pressure test during wakefulness for evaluating the risk of obstructive sleep apnea in patients referred for sleep studies. Clinics Sao Paulo — Lim J, Alshaer H, Ghahjaverestan NM, Bradley TD Relationship between airflow limitation in response to upper airway negative pressure during wakefulness and obstructive sleep apnea severity.

Sleep Breath —9. Gao J, Yang Y, Lin P, Park DS Computer vision in healthcare applications. J Healthc Eng —5. Siddiqi MH, Khan K, Khan RU, Alsirhani A Face image analysis using machine learning: a survey on recent trends and applications.

Electronics Okubo M, Suzuki M, Horiuchi A et al Morphologic analyses of mandible and upper airway soft tissue by MRI of patients with obstructive sleep apnea hypopnea syndrome. Miles PG, Vig PS, Weyant RJ et al Craniofacial structure and obstructive sleep apnea syndrome—a qualitative analysis and meta-analysis of the literature.

Am J Orthod Dentofacial Orthop — Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT et al Speech signal and facial image processing for obstructive sleep apnea assessment. Comput Math Methods Med — Tyan M, Espinoza-Cuadros F, Pozo RF et al Obstructive sleep apnea in women: study of speech and craniofacial characteristics.

JMIR Mhealth Uhealth 5:e Lee RWW, Petocz P, Prvan T et al Prediction of obstructive sleep apnea with craniofacial photographic analysis. Balaei AT, Sutherland K, Cistulli PA, de Chazal P Automatic detection of obstructive sleep apnea using facial images.

In: IEEE 14th International symposium on biomedical imaging ISBI Sutherland K, Lee RWW, Petocz P et al Craniofacial phenotyping for prediction of obstructive sleep apnoea in a Chinese population. Respirology — Monna F, Ben Messaoud R, Navarro N et al Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans.

Ciaparrone G, Chiariglione L, Tagliaferri R A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos.

Neural Comput Appl — Islam SMS, Mahmood H, Al-Jumaily AA, Claxton S Deep learning of facial depth maps for obstructive sleep apnea prediction. In: International conference on machine learning and data engineering iCMLDE. Dae GJ, Hae YC, Grunstein RR, Yee B Predictive value of Kushida index and acoustic pharyngometry for the evaluation of upper airway in subjects with or without obstructive sleep apnea.

J Korean Med Sci — Kushida CA, Efron B, Guilleminault C A predictive morphometric model for the obstructive sleep apnea syndrome. Ann Intern Med — Gelardi M, Del Giudice AM, Cariti F et al Acoustic pharyngometry: clinical and instrumental correlations in sleep disorders.

Braz J Otorhinolaryngol — DeYoung PN, Bakker JP, Sands SA et al Acoustic pharyngometry measurement of minimal cross-sectional airway area is a significant independent predictor of moderate-to-severe obstructive sleep apnea.

J Clin Sleep Med — Shivarov G Anthropometry and acoustic pharyngometry of the oral cavity in sleep-disordered breathing. J IMAB — Ann Proc Sci Pap — Salisbury JI, Sun Y Rapid screening test for sleep apnea using a nonlinear and nonstationary signal processing technique.

Med Eng Phys — Simply RM, Dafna E, Zigel Y Obstructive sleep apnea OSA classification using analysis of breathing sounds during speech. In: 26th European signal processing conference EUSIPCO.

Moussavi Z, Elwali A, University of Manitoba System and methods for screening obstructive sleep apnea during wakefulness using anthropometric information and tracheal breathing sounds. Elwali A, Moussavi Z Predicting polysomnography parameters from anthropometric features and breathing sounds recorded during wakefulness.

Diagnostics Hajipour F, Moussavi Z Spectral and higher order statistical characteristics of expiratory tracheal breathing sounds during wakefulness and sleep in people with different levels of obstructive sleep apnea.

J Med Biol Eng — Elwali A, Moussavi Z Determining breathing sound features representative of obstructive sleep apnea during wakefulness with least sensitivity to other risk factors.

Elwali A, Moussavi Z A novel decision making procedure during wakefulness for screening obstructive sleep apnea using anthropometric information and tracheal breathing sounds.

Sci Rep Montazeri A, Giannouli E, Moussavi Z Assessment of obstructive sleep apnea and its severity during wakefulness. Ann Biomed Eng — Hajipour F, Jozani MJ, Moussavi Z A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea.

Med Biol Eng Comput — Elwali A, Meza-Vargas S, Moussavi Z Using tracheal breathing sounds and anthropometric information for screening obstructive sleep apnoea during wakefulness. J Med Eng Technol — Hajipour F, Jozani MJ, Elwali A, Moussavi Z Regularized logistic regression for obstructive sleep apnea screening during wakefulness using daytime tracheal breathing sounds and anthropometric information.

Pasterkamp H Posture-dependent change of tracheal sounds at standardized flows in patients with obstructive sleep apnea. Chest J Sola-Soler J, Fiz JA, Torres A, Jane R Identification of obstructive sleep apnea patients from tracheal breath sound analysis during wakefulness in polysomnographic studies.

In: 36th Annual international conference of the ieee engineering in medicine and biology society. Sola-Soler J, Jane R, Fiz JA, Morera J Formant frequencies of normal breath sounds of snorers may indicate the risk of obstructive sleep apnea syndrome.

In: 30th Annual international conference of the IEEE engineering in medicine and biology society. Pozo RF, Murillo JLB, Gmez LH et al Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques.

EURASIP J Adv Sig Process — Montero Benavides A, Fernández Pozo R, Toledano DT et al Analysis of voice features related to obstructive sleep apnoea and their application in diagnosis support. Comput Speech Lang — Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT et al Reviewing the connection between speech and obstructive sleep apnea.

Biomed Eng Online — Montero Benavides A, Blanco Murillo JL, Fernández Pozo R et al Formant frequencies and bandwidths in relation to clinical variables in an obstructive sleep apnea population. J Voice — Goldshtein E, Tarasiuk A, Zigel Y Automatic detection of obstructive sleep apnea using speech signals.

IEEE Trans Biomed Eng — Simply RM, Dafna E, Zigel Y Diagnosis of obstructive sleep apnea using speech signals from awake subjects. IEEE J Sel Top Sign Proces — Solé-Casals J, Munteanu C, Martín OC et al Detection of severe obstructive sleep apnea through voice analysis.

Appl Soft Comput — Pang K-G, Hsung T-C, Liao G et al Obstructive sleep apnea detection using speech signals with high frequency components.

J Commun — Yılmaz D, Yıldız M, Uyar Toprak Y, Yetkin S Obstructive sleep apnea detection with nonlinear analysis of speech. Biomed Signal Process Control Ramachandran SK, Josephs LA A meta-analysis of clinical screening tests for obstructive sleep apnea.

Behar JA, Palmius N, Daly J et al Sleep questionnaires in screening for obstructive sleep apnoea. Comput Cardiol —4.

Kump K, Whalen C, Tishler PV et al Assessment of the validity and utility of a sleep-symptom questionnaire. Pataka A, Daskalopoulou E, Kalamaras G et al Evaluation of five different questionnaires for assessing sleep apnea syndrome in a sleep clinic. Solecka Š, Matler K, Kostlivý T et al A comparison of the reliability of five sleep questionnaires for the detection of obstructive sleep apnea.

Shahar E, Whitney CW, Redline S et al Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study.

Caseiro P, Fonseca-Pinto R, Andrade A Screening of obstructive sleep apnea using Hilbert-Huang decomposition of oronasal airway pressure recordings. Young T, Palta M, Dempsey J et al The occurrence of sleep-disordered breathing among middle-aged adults.

N Engl J Med — Simas R, Maestri F, Normando D Controlling false positive rates in research and its clinical implications. Dental Press J Orthod Olliaro P, Torreele E Managing the risks of making the wrong diagnosis: first, do no harm.

Int J Infect Dis Burke JF, Sussman JB, Kent DM, Hayward RA Three simple rules to ensure reasonably credible subgroup analyses. The BMJ —5. Kim HE, Kim HH, Han BK et al Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study.

Lancet Digit Health 2:e—e Meshkinfamfard S, Gorban A, Tyukin I Tackling rare false-positives in face recognition: a case study. Shen Y, Shamout FE, Oliver JR et al Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun — Alsubie HS, BaHammam AS Obstructive sleep apnoea: children are not little adults.

Paediatr Respir Rev — Download references. This review study was funded by the Natural Sciences and Engineering Research Council of Canada NSERC and the Canadian Institutes of Health Research CIHR.

Biomedical Engineering Program, University of Manitoba, 66 Chancellors Cir, Winnipeg, MB, R3T 2N2, Canada. Biomedical Engineering Program, Marian University, Cold Sprint Road, Indianapolis, IN, , USA.

Electrical and Computer Engineering Department, University of Manitoba, 66 Chancellors Cir, Winnipeg, MB, R3T 2N2, Canada. Biosystems Engineering Department, University of Manitoba, 66 Chancellors Cir, Winnipeg, MB, R3T 2N2, Canada.

You can also search for this author in PubMed Google Scholar. Conceptualization: AMA, AE, and ZM; methodology: AMA, AE, BK, and FH; writing and original draft preparation: AMA and AE; writing, review, and editing: AMA, AE FH, NJ and ZM; supervision: ZM; project administration: ZM; funding acquisition: ZM through the Natural Sciences and Engineering Research Council of Canada NSERC and the Canadian Institutes of Health Research CIHR funding.

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Graphical abstract. Clinician-Focused Overview and Developments in Polysomnography Article 23 November The effect of CPAP therapy on heart rate variability in patients with obstructive sleep apnea Article Open access 14 September Use our pre-submission checklist Avoid common mistakes on your manuscript.

Full size image. Table 1 List of abbreviations used in this paper in alphabetical order Full size table. Flow diagram of paper inclusion and exclusion. Distribution of articles over topics in this review. OSA detection during wakefulness technologies. Detailed process of medical image analysis.

Table 2 Summary of key findings of the investigated papers for medical imaging technique Full size table. Table 3 Summary of key findings of the investigated papers for NEP Full size table.

Table 4 Summary of key findings of the investigated papers for facial image landmarks Full size table. Table 5 Summary of key findings of the investigated papers for pharyngometer and nasal airway pressure Full size table.

Table 6 Summary of key findings of the investigated papers for breathing sounds Full size table. Table 7 Summary of key findings of the investigated papers for speech analysis Full size table.

Table 8 Summary of key findings of the investigated papers for the questionnaire technique Full size table. Table 9 Summary of methodology in reviewed works based on method characteristics Full size table. Sleep-Disordered Breathing: Diagnosis Chapter © Obstructive Sleep Apnea: Diagnosis with Polysomnography and Portable Monitors Chapter © A Retrospective Study on Obstructive Sleep Apnea Chapter © References Colten HR, Altevogt BM Sleep disorders and sleep deprivation: an unmet public health problem.

American Academy of Sleep Medicine, Westchester, IL Berry RB, Brooks R, Gamaldo CE et al The AASM manual for the scoring of sleep and associated events.

Rules Terminology Techn Specifications Darien Illinois Am Acad Sleep Med Google Scholar Bradley TD, Floras JS Sleep apnea: implications in cardiovascular and cerebrovascular disease, 2nd edn.

CRC Press, Boca Raton Google Scholar Butt M, Dwivedi G, Khair O, Lip GYH Obstructive sleep apnea and cardiovascular disease. RV Article PubMed PubMed Central CAS Google Scholar Noda A, Yasuma F, Miyata S et al Sleep fragmentation and risk of automobile accidents in patients with obstructive sleep apnea—sleep fragmentation and automobile accidents in OSA.

Health N Hav — Google Scholar Young T, Finn L, Peppard PE et al Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin Sleep Cohort. Harvard Medical School Division of Sleep Medicine Boston, Boston, MA Google Scholar Tregear S, Reston J, Schoelles K, Phillips B Obstructive sleep apnea and risk of motor vehicle crash: systematic review and meta-analysis.

OpenStax, Houston, TX Elwali A, Moussavi Z Obstructive sleep apnea screening and airway structure characterization during wakefulness using tracheal breathing sounds. Med Eng Phys — Article PubMed CAS Google Scholar Young T, Palta M, Dempsey J et al The occurrence of sleep-disordered breathing among middle-aged adults.

EBO Article PubMed PubMed Central Google Scholar Olliaro P, Torreele E Managing the risks of making the wrong diagnosis: first, do no harm. Sleep-onset insomnia suggests delayed sleep phase syndrome Circadian rhythm sleep disorder, altered sleep phase types Circadian rhythm sleep disorders are caused by desynchronization between internal sleep-wake rhythms and the light-darkness cycle.

read more , chronic psychophysiologic insomnia, restless legs syndrome Periodic Limb Movement Disorder PLMD and Restless Legs Syndrome RLS Periodic limb movement disorder PLMD and restless legs syndrome RLS are characterized by abnormal motions of and, for RLS, usually sensations in the lower or upper extremities, which may read more , or childhood phobias.

Sleep maintenance insomnia suggests major depression Major depressive disorder unipolar depressive disorder Depressive disorders are characterized by sadness severe enough or persistent enough to interfere with function and often by decreased interest or pleasure in activities. read more , central sleep apnea Central Sleep Apnea Central sleep apnea CSA is a heterogeneous group of conditions characterized by changes in ventilatory drive without airway obstruction.

read more , obstructive sleep apnea Obstructive Sleep Apnea OSA Obstructive sleep apnea OSA consists of multiple episodes of partial or complete closure of the upper airway that occur during sleep and lead to breathing cessation defined as a period of read more , or aging.

Falling asleep early and awakening early suggest advanced sleep phase syndrome Circadian rhythm sleep disorder, altered sleep phase types Circadian rhythm sleep disorders are caused by desynchronization between internal sleep-wake rhythms and the light-darkness cycle.

Clinicians should suspect obstructive sleep apnea in patients with significant snoring, frequent awakenings, and other risk factors. The STOP-BANG score can help predict risk of obstructive sleep apnea see table STOP-BANG Risk Score for Obstructive Sleep Apnea STOP-BANG Risk Score for Obstructive Sleep Apnea.

Tests are usually done when specific symptoms or signs suggest obstructive sleep apnea, nocturnal seizures, narcolepsy, periodic limb movement disorder, or other disorders whose diagnosis relies on identification of characteristic polysomnographic findings. Tests are also done when the clinical diagnosis is in doubt or when response to initial presumptive treatment is inadequate.

If symptoms or signs strongly suggest certain causes eg, restless legs syndrome, poor sleep habits, transient stress, shift work disorder , testing is not required. Polysomnography is particularly useful when obstructive sleep apnea, narcolepsy, nocturnal seizures, periodic limb movement disorder, or parasomnias are suspected.

It also helps clinicians evaluate violent and potentially injurious sleep-related behaviors. It monitors brain activity via EEG , eye movements, heart rate, respirations, oxygen saturation, and muscle tone and activity during sleep.

Video recording may be used to identify abnormal movements during sleep. Polysomnography is typically done in a sleep laboratory; home sleep studies are now commonly used to diagnose obstructive sleep apnea, but not other sleep disorders 1 Evaluation reference Almost half of all people in the US report sleep-related problems.

Disordered sleep can cause emotional disturbance, memory difficulty, poor motor skills, decreased work efficiency, and increased Patients lie in a darkened room and are asked to sleep. Onset and stage of sleep including REM are monitored by polysomnography to determine the degree of sleepiness.

For the maintenance of wakefulness test , patients are asked to stay awake in a quiet room during 4 wakefulness opportunities 2 hours apart while they sit in a bed or a recliner.

Rosen IM, Kirsch DB, Chervin RD, et al : Clinical Use of a Home Sleep Apnea Test: An American Academy of Sleep Medicine Position Statement. J Clin Sleep Med 13 10 —, doi: Specific conditions are treated.

The primary treatment for insomnia is cognitive-behavioral therapy, which ideally should be done before hypnotics are prescribed. Good sleep hygiene Sleep Hygiene is a component of cognitive-behavioral therapy that is important whatever the cause and is often the only treatment patients with mild problems need.

Cognitive-behavioral therapy for insomnia focuses on managing the common thoughts, worries, and behaviors that interfere with sleep.

It is typically done in 4 to 8 individual or group sessions but can be done remotely online or by telephone; however, evidence for the effectiveness of remote therapy is weaker. Helping patients improve sleep hygiene Sleep Hygiene , particularly restricting time spent in bed, establishing a regular sleep schedule, and controlling stimuli.

Teaching patients about the effects of sleeplessness and helping them identify inappropriate expectations about how much sleep they should get.

Restricting the amount of time spent in bed aims to limit the time patients spend lying in bed trying unsuccessfully to sleep. Patients are asked to get out of bed in the morning at a fixed time and then calculate a bed time based on total sleep time.

After a week, this approach typically improves quality of sleep. Then, the time spent in bed can be increased by gradually making bed time earlier, as long as awakenings in the middle of the night remain minimal. General guidelines for use of hypnotics see table Guidelines for the Use of Hypnotics Guidelines for the Use of Hypnotics aim at minimizing abuse, misuse, and addiction.

For commonly used hypnotics, see table Oral Hypnotics in Common Use Oral Hypnotics in Common Use. All hypnotics except ramelteon , low-dose doxepin , and suvorexant act at the benzodiazepine recognition site on the gamma -aminobutyric GABA receptor and augment the inhibitory effects of GABA.

Hypnotics differ primarily in elimination half-life and onset of action. Drugs with a short half-life are used for sleep-onset insomnia. Drugs with a longer half-life are useful for both sleep-onset and sleep maintenance insomnia, or, in the case of low-dose doxepin , only for sleep maintenance insomnia.

New drugs with a very short duration of action eg, low-dose sublingual zolpidem can be taken in the middle of the night, during a nocturnal awakening, as long as patients stay in bed for at least 4 hours after use. Patients who experience daytime sedation, incoordination, or other daytime effects should avoid activities requiring alertness eg, driving , and the dose should be reduced, the drug stopped, or, if needed, another drug used.

Other adverse effects include amnesia, hallucinations, incoordination, and falls. Falling is a significant risk with all hypnotics. Three dual orexin receptor antagonists daridorexant , lemborexant , suvorexant can be used to treat sleep-onset and maintenance insomnia.

They block orexin receptors in the brain, thereby blocking orexin-induced wakefulness signals and enabling sleep initiation. Dual orexin receptor antagonists block orexin receptors-1 and The orexin receptor-1 is involved in suppressing the onset of rapid eye movement REM sleep; the orexin receptor-2 is involved in suppressing non-REM sleep onset and, to some extent, in controlling REM sleep.

However, the mechanism of action for dual orexin receptor antagonists is not fully understood. For suvorexant , the recommended dose is 10 mg, taken no more than once a night and taken within 30 minutes of going to bed, with at least 7 hours before the planned time of awakening. The dose can be increased but should not to exceed 20 mg once a day.

The most common adverse effect is somnolence. Lemborexant 5 mg is taken once a day within 30 minutes of going to bed; the dose can be increased to 10 mg maximum dose based on patient response and tolerability. Daridorexant 25 to 50 mg is taken once a day within 30 minutes of going to bed.

Daridorexant has the shortest half-life 8 hours of the dual oxexin receptor antagonists. Hypnotics should be used cautiously in patients with pulmonary insufficiency. In older patients, any hypnotic, even in small doses, can cause restlessness, excitement, falls, or exacerbation of delirium and dementia.

Rarely, hypnotics can cause complex sleep-related behaviors, such as sleepwalking and even sleep driving; use of higher-than-recommended doses and concurrent consumption of alcoholic beverages may increase risk of such behaviors.

Rarely, severe allergic reactions occur. Prolonged use of hypnotics Sedatives Sedatives include benzodiazepines, barbiturates, and related drugs. High doses can cause decreased level of consciousness and respiratory depression, which may require intubation and mechanical read more is typically discouraged because tolerance can develop and because abrupt discontinuation can cause rebound insomnia or even anxiety, tremor, and seizures.

These effects are more common with benzodiazepines particularly triazolam and less common with nonbenzodiazepines. Difficulties can be minimized by using the lowest effective dose for brief periods and by tapering the dose before stopping the drug see also Withdrawal and detoxification Withdrawal and detoxification Sedatives include benzodiazepines, barbiturates, and related drugs.

Alcohol is used by many patients to help with sleep, but alcohol is a poor choice because it produces unrefreshing, disturbed sleep with frequent nocturnal awakenings, often increasing daytime sleepiness. Alcohol can further impair respiration during sleep in patients with obstructive sleep apnea and other pulmonary disorders such as chronic obstructive pulmonary disease COPD.

Over-the-counter OTC antihistamines eg, doxylamine , diphenhydramine can induce sleep. However, efficacy is unpredictable, and these drugs have long a half-life and have adverse effects such as daytime sedation, confusion, urinary retention, and other systemic anticholinergic effects, which are particularly worrisome in older people.

Over-the-counter antihistamines should not be used to treat insomnia. Antidepressants taken in low doses at bedtime eg, doxepin 25 to 50 mg, paroxetine 5 to 20 mg, trazodone 50 mg, trimipramine 75 to mg may improve sleep. However, antidepressants should be used in these low doses mainly when standard hypnotics are not tolerated rare or in higher antidepressant doses when depression is present.

Ultra low dose doxepin 3 or 6 mg is indicated for sleep maintenance insomnia. Melatonin is a hormone that is secreted by the pineal gland and that occurs naturally in some foods. Darkness stimulates secretion, and light inhibits it. By binding with melatonin receptors in the suprachiasmatic nucleus, melatonin mediates circadian rhythms, especially during physiologic sleep onset.

Oral melatonin typically 0. When used to treat this disorder, it must be taken at the appropriate time a few hours before the evening increase in endogenous melatonin secretion—in early evening for most people, typically 3 to 5 hours before the intended bedtime and at a low dose of 0.

For other forms of insomnia, melatonin 's efficacy is largely unproved. Melatonin can cause headache, dizziness, nausea, and drowsiness.

However, after widespread use, no other worrisome adverse effects have been reported. Available preparations of melatonin are unregulated, so content and purity cannot be ensured, and the effects of long-term use are unknown.

Cannabinoids Marijuana Cannabis Marijuana is a euphoriant that can cause sedation or dysphoria in some users. Psychologic dependence can develop with chronic use, but very little physical dependence is clinically apparent read more include the following:. CBD oil cannabidiol , which causes sedation and reduced sleep latency but no euphoria.

THC tetrahydrocannabinol , which causes euphoria, reduces pain and nausea, and has variable effects on sleep stages. Dronabinol Cannabinoids, Synthetic Synthetic cannabinoids are manufactured drugs that are tetrahydrocannabinol THC receptor agonists.

They are typically applied to dried plant material and smoked. THC is the primary active read more , which is a synthetic analog. Poor sleep hygiene and situational disruptors eg, shift work, emotional stressors are common causes of insomnia. Consider medical disorders eg, sleep apnea syndromes, pain disorders and psychiatric disorders eg, mood disorders as possible causes.

Usually, consider sleep studies eg, polysomnography when sleep apnea syndromes, periodic limb movements, or other sleep disorders are suspected, when the clinical diagnosis is in doubt, or when response to initial presumptive treatment is inadequate.

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IN THIS TOPIC. OTHER TOPICS IN THIS CHAPTER. Periodic Limb Movement Disorder PLMD and Restless Legs Syndrome RLS. Approach to the Patient With a Sleep or Wakefulness Disorder By Richard J. View PATIENT EDUCATION. Pathophysiology Etiology Evaluation Treatment Key Points.

Sleep Myths. There are 2 states of sleep, each marked by characteristic physiologic changes:. Nonrapid eye movement NREM EEG These EEG tracings show characteristic theta waves, sleep spindles, and K complexes during stages 1 N1 , 2 N2 , and 3 N3 NREM sleep. Rapid eye movement REM EEG This figure includes an EEG tracing showing characteristic sawtooth waves and an eye tracing showing rapid eye movements , which occur during REM sleep.

Typical sleep pattern in young adults Rapid eye movement REM sleep occurs cyclically throughout the night every 90— min. An insomnia disorder eg, adjustment sleep disorder, psychophysiologic insomnia.

Psychiatric disorders, particularly mood, anxiety, and substance use disorders. Exercise or excitement eg, a thrilling television show late in the evening. History History of present illness should include duration and age at onset of symptoms and any events eg, a life or work change, new drug, new medical disorder that coincided with onset.

Review of systems should check for symptoms of specific sleep disorders, including. Bed partners or other family members can best identify some of these symptoms. Obesity with fat distributed around the neck or midriff. Modified Mallampati scoring Modified Mallampati scoring is as follows:.

Class 1: Tonsils, uvula, and soft palate are fully visible. Class 2: Hard and soft palate, upper portion of tonsils, and uvula are visible. The following findings are of particular concern:.

Falling asleep while driving or other potentially dangerous situations. Patients with EDS may require laboratory tests of renal, liver, and thyroid function. When benzodiazepines are to be stopped, they should be tapered and not stopped abruptly.

Many drugs not specifically indicated for insomnia are used to induce and maintain sleep. CBN cannabinol , which causes sedation, reduces pain, and increases appetite. Whether cannabis is effective for insomnia is unclear, but it is useful for chronic pain.

Tolerance can develop; stopping cannabis after long-term use results in insomnia. Good sleep hygiene, sometimes as part of cognitive-behavioral therapy, is first-line treatment.

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Sleep apnea wakefulness Maintenance of Wakefulness Test MWT Lean muscle tone used to measure how alert you are sleep the day. It shows whether you can slee; awake for waakefulness sleep apnea wakefulness period of time. The test is based on the idea that, in some cases, your ability to stay awake may be more important than how fast you fall asleep. This is an indicator of how well you can function and remain alert in quiet times of inactivity. The test is performed in a dark room that is quiet. Obstructive sleep apnea Slleep is sleep apnea wakefulness chronic slesp affecting up to 1 wakkefulness people, globally. Despite this sleep apnea wakefulness, OSA is still Blood sugar levels to sleep apnea wakefulness underdiagnosed. Lack wakefulmess diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake wakefulness is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness.

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