0459 Diagnostic Performance of Symptomless Obstructive Sleep Apnea Prediction Tools in Clinical and Community-based Samples

Holfinger, Steven; Lyons, M. Melanie; Mindel, Jesse; Cistulli, Peter A.; Sutherland, Kate; Chen, Ning‐Hung; McArdle, Nigel; Gíslason, Þórarinn; Penzel, Thomas; Han, Fang; Li, Qing Y; Mazzotti, Diego R.; Keenan, Brendan T.; Pack, AI; Magalang, Ulysses J.

doi:10.1093/sleep/zsz067.458

Cited by 4 publications

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“…Advances in technology such as the development of telemedicine, in-home diagnosis, development of new diagnostic criteria and techniques (use of electronic medical records or machine learning technology), 'wearable technology' and cloud-based or connected devices, mass-produced or locally based therapies may allow for the opportunity to better address the health and economic consequences of OSA and optimize the availability and efficiency of treatment. 168,169 A better understanding of patient endotypes and symptom clusters of clinical phenotypes is needed for a more personalized approach to risk, diagnosis and treatment. This would also allow for the development of new simplified diagnostic and efficient treatment care delivery pathways and models using local personnel and resources in collaboration with local and distant sleep medicine expertise.…”

Section: Discussionmentioning

confidence: 99%

“…The traditional approach to assessing risk, diagnosis and treatment is unlikely to be able to meet this demand. Advances in technology such as the development of telemedicine, in‐home diagnosis, development of new diagnostic criteria and techniques (use of electronic medical records or machine learning technology), ‘wearable technology’ and cloud‐based or connected devices, mass‐produced or locally based therapies may allow for the opportunity to better address the health and economic consequences of OSA and optimize the availability and efficiency of treatment . A better understanding of patient endotypes and symptom clusters of clinical phenotypes is needed for a more personalized approach to risk, diagnosis and treatment.…”

Section: Discussionmentioning

confidence: 99%

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Global burden of sleep‐disordered breathing and its implications

et al. 2020

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One‐seventh of the world's adult population, or approximately one billion people, are estimated to have OSA. Over the past four decades, obesity, the main risk factor for OSA, has risen in striking proportion worldwide. In the past 5 years, the WHO estimates global obesity to affect almost two billion adults. A second major risk factor for OSA is advanced age. As the prevalence of the ageing population and obesity increases, the vulnerability towards having OSA increases. In addition to these traditional OSA risk factors, studies of the global population reveal select contributing features and phenotypes, including extreme phenotypes and symptom clusters that deserve further examination. Untreated OSA is associated with significant comorbidities and mortality. These represent a tremendous threat to the individual and global health. Beyond the personal toll, the economic costs of OSA are far‐reaching, affecting the individual, family and society directly and indirectly, in terms of productivity and public safety. A better understanding of the pathophysiology, individual and ethnic similarities and differences is needed to better facilitate management of this chronic disease. In some countries, measures of the OSA disease burden are sparse. As the global burden of OSA and its associated comorbidities are projected to further increase, the infrastructure to diagnose and manage OSA will need to adapt. The use of novel approaches (electronic health records and artificial intelligence) to stratify risk, diagnose and affect treatment are necessary. Together, a unified multi‐disciplinary, multi‐organizational, global approach will be needed to manage this disease.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Global burden of sleep‐disordered breathing and its implications

et al. 2020

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“…Abnormal breathing patterns may also indicate the potential for injury or metabolic illnesses, and these patterns may also reflect emotional imbalance [ 6 ] and stress [ 7 ]. Several clinical studies propose that abnormal breathing patterns can forecast specific diseases [ 8 ], thus providing comprehensive evidence for medical treatment [ 9 ]. Therefore, vigilant observation of the breathing rate and pattern is critical in the analysis and treatment of numerous diseases [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Rehman

Shah

Khan

et al. 2021

Sensors

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Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.

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“…Noninvasive and unobtrusive methodologies are more preferred when it comes to detection of Covid-19 symptoms. Multiple devices which promote contact-less monitoring of respiratory organs are available in the form of image sensors, doppler radar and depth and thermal cameras (Holfinger et al, 2019;Sohrabi et al, 2020;Chow et al, 2020;AL-Khalidi et al, 2011;LeCun and Bengio, 1995). Thermal imaging cameras are mostly preferred because of its low cost and immediate results.…”

Section: Introductionmentioning

confidence: 99%

Pervasive computational model and wearable devices for prediction of respiratory symptoms in progression of COVID-19

Dhanapal¹,

Narayanamurthy

Shanmugam³

et al. 2020

IJPCC

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Purpose The purpose of this paper is to provide a model for prediction of respiratory symptoms in the progression of COVID-19, social distancing, frequent hand washes, wearing of face mask in public are some of the potential measures of preventing the disease from further spreading. In spite of the effects and efforts taken by governments, the pandemic is still uncontrolled in major cities of the world. The proposed technique in this paper introduces a non-intrusive and major screening of vital symptoms and changes in the respiratory organs. Design/methodology/approach The novel coronavirus or Covid-19 has become a serious threat to social and economic growth of many nations worldwide. The pace of progression was significantly higher in the past two months. Identified by severe respiratory illness, fever and coughs, the disease has been threatening the lives of human society. Early detection and prognosis is absolutely necessary to isolate the potential spreaders of the disease and to control the rate of progression. Findings Recent studies have highlighted the changes observed in breathing characteristics of infected patients. Respiratory pattern of Covid-19 patients can be differentiated from the respiratory pattern of normal cold/flu affected patients. Tachypnoea is one among the vital signs identified to be distinguishing feature of Covid-19. The proposed respiratory data capture will commence with facial recognition, use of infrared sensors and machine-learning approaches to classify the respiratory patterns, which finally narrows down as a symptom of Covid-19. Originality/value Proposed system produced outcome of 94% accuracy, precision, recall and a F1-measure as an average in the conducted experiments. This method also proves to be a fruitful solution for large-scale monitoring and categorisation of people based on the symptoms.

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0459 Diagnostic Performance of Symptomless Obstructive Sleep Apnea Prediction Tools in Clinical and Community-based Samples

Cited by 4 publications

References 0 publications

Global burden of sleep‐disordered breathing and its implications

Global burden of sleep‐disordered breathing and its implications

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Pervasive computational model and wearable devices for prediction of respiratory symptoms in progression of COVID-19

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