0465 Performance of Revised Machine Learning Models for Prediction of Non-Diagnostic Home Sleep Apnea Tests

Stretch, Robert; Maller, Abigail; Hwang, Dennis; Ryden, Armand; Fung, Constance H.; Zeidler, Michelle

doi:10.1093/sleep/zsz067.464

Cited by 2 publications

(1 citation statement)

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“…Considering the implications that the sleep apnea can lead to the people as discussed in the Figure 2, the need in terms of focusing on the early detection models and the prevention models too are highly important. [3] Currently, the popular model of diagnosis for the sleep apnea is the sleep study test which the patient undergoes in a clinical set-up. Based on the AHI levels that are resulting in the studies, the presence and the intensity of the sleep apnea is decided for the patients.…”

Section: Effects Of Sleep Apneamentioning

confidence: 99%

Predictive Analytics for Obstructive Sleep Apnea Detection

2019

IJITEE

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many of the research studies that have focused on the issue of sleep apnea conditions among the people, emphasize the fact that the numbers are rising in significant numbers year on year. Profoundly, identifying symptoms in the patients is very important to ascertain the possible impact of sleep apnea in patients. The researchers in earlier studies have focused on the conditions of systematical physical examination over the patients who are prone to physical examination for head and neck aches, has relative impact of the osa conditions and also on some scoring-based models using the machine learning solutions. The scope of a new model could be about identification of the features in two stage model. The first stage could be about understanding the lifestyle and psychological conditions of the patient data and accordingly choose the metrics and the model of osa detection tool that can be used for analysis. If such comprehensive approach can be developed, it can be effective process for developing a sustainable solution.

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Section: Effects Of Sleep Apneamentioning

confidence: 99%

Predictive Analytics for Obstructive Sleep Apnea Detection

2019

IJITEE

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Environmental Benefits of Sleep Apnoea Detection in the Home Environment

et al. 2022

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Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.

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0465 Performance of Revised Machine Learning Models for Prediction of Non-Diagnostic Home Sleep Apnea Tests

Cited by 2 publications

References 0 publications

Predictive Analytics for Obstructive Sleep Apnea Detection

Predictive Analytics for Obstructive Sleep Apnea Detection

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

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