Severity of obstructive sleep apnea (OSA) in 382 Han and Uyghur patients with sleep disorders

Gul, Aman; Memtily, Nassirhadjy; Aihemaiti, Aikeranmu; Abdukadir, Amir; Wushuer, Palidan

doi:10.1142/s2575900018500106

Cited by 1 publication

(1 citation statement)

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“…The dataset included 42 healthy subjects and 97 patients with OSA. The patients with OSA were further subdivided into mild to moderate (mtom) and severe groups according to clinical indices evaluating the severity ( Gul et al, 2018 ), and the values in each group for Respiratory Disturbance Index (RDI) and blood oxygen saturation (SpO 2 ), which are key clinical indices, are presented in Table 1 . To examine only the effects of OSA, all subjects with other medical histories such as stroke, neurological disorders, alcoholism, cancer, hypertension, and thyroid problems were excluded.…”

Section: Methodsmentioning

confidence: 99%

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening

Kang

Kim

et al. 2023

Front. Neurosci.

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IntroductionSleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease.MethodsThe first crucial step in evaluating individuals’ quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance.ResultsThe performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures.DiscussionThe current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals’ sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

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Section: Methodsmentioning

confidence: 99%