Abstract
Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features.
Highlights
Obstructive sleep apnea (OSA) and insomnia are both common sleep disorders that generally coexist in patients [1]
Because hospital-based sleep parameters, such as the apnea–hypopnea index (AHI) score, percentage of each sleep stage, and total sleep time (TST; measured using brainwave signals), cannot be determined without measurement by a sleep technician, these parameters were not employed in model construction
Because the ODI3% was derived from the TST and may not be accurately determined without lab-PSG, we introduced a surrogate index, the total recording time ODI-3% (ODI-3%-TRT), to infer oxygen desaturation severity; the total number of oxygen desaturation events (>3%) was calculated and divided by the total recording time
Summary
Obstructive sleep apnea (OSA) and insomnia are both common sleep disorders that generally coexist in patients [1]. A review in 2019 of 37 related studies documented that the worldwide prevalence of insomnia in patients with OSA ranged from 18% to 42% [2]. A population-based epidemiological study investigated the cultural factors that affect insomnia by comparing the prevalence of insomnia in adolescents in the United States and in Hong Kong, and demonstrated similar estimates of approximately 9.3% [3]. A 2021 review reported that the reduced physical activity and elevated food intake due to the pandemic and consequent lockdowns were associated with an increase in the obese population, which increased the risk of having OSA [5]. The appropriate medical approaches for treating these sleep disorders must be discerned
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