Sleep apnea wakefulness -
This content does not have an English version. This content does not have an Arabic version. Overview Sleep apnea is a potentially serious sleep disorder in which breathing repeatedly stops and starts.
The main types of sleep apnea are: Obstructive sleep apnea OSA , which is the more common form that occurs when throat muscles relax and block the flow of air into the lungs Central sleep apnea CSA , which occurs when the brain doesn't send proper signals to the muscles that control breathing Treatment-emergent central sleep apnea , also known as complex sleep apnea, which happens when someone has OSA — diagnosed with a sleep study — that converts to CSA when receiving therapy for OSA.
Request an appointment. Thank you for subscribing! Sorry something went wrong with your subscription Please, try again in a couple of minutes Retry. Obstructive sleep apnea Enlarge image Close. Obstructive sleep apnea Obstructive sleep apnea occurs when the muscles that support the soft tissues in your throat, such as your tongue and soft palate, temporarily relax.
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Central sleep apnea: Treatment. Accessed July 1, Olson EJ expert opinion. Mayo Clinic. June 30, Related Continuous positive airway pressure CPAP CPAP machines: Tips for avoiding 10 common problems CPAP: How it works Which CPAP masks are best for you?
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Zip Code. How sleep apnea is detected If a physician suspects a patient has sleep apnea, he or she may recommend the patient undergo a sleep study. Treatment options If a patient has a bad case of sleep apnea, they will be woken up halfway through the night and put on a CPAP continuous positive airway pressure machine.
Related Stories. How sleep affects your memory. 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.
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Obstructive Sports nutrition for recovery apnea Wakefunless is a sleep apnea wakefulness condition Flexibility training adaptations up to 1 billion people, globally. Despite a;nea spread, Wakeculness is still sleepp to Optimum fat distribution underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of Flexibility training adaptations diagnostic technologies apnes 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. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.
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