Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features

Tsai, Cheng-Yu; Kuan, Yi‐Chun; Hsu, Wei-Han; Lin, Yi; Hsu, Chia-Rung; Lo, Kang; Hsu, Wen-Hua; Majumdar, Arnab; Liu, Yi-Shin; Hsu, Shin-Mei; Ho, Shu-Chuan; Cheng, Wun-Hao; Lin, Shang‐Yang; Lee, Kang‐Yun; Wu, Dean; Lee, Hsin Chien; Wu, Chia-Chang; Liu, Wen‐Te

doi:10.3390/diagnostics12010050

Cited by 9 publications

(6 citation statements)

References 71 publications

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“… 34 These ensemble learning approaches, thus, have been widely utilized to assist diagnosis and decision-making in different medical fields. 30 , 35 …”

Section: Discussionmentioning

confidence: 99%

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

et al. 2023

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Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

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“… 34 These ensemble learning approaches, thus, have been widely utilized to assist diagnosis and decision-making in different medical fields. 30 , 35 …”

Section: Discussionmentioning

confidence: 99%

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

et al. 2023

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“…Moreover, the bootstrapping procedure and the number of decision trees in RF can be easily fine-tuned to avoid overfitting and maintain model stability [23]. Hence, the RF approach has been broadly employed for aiding medical diagnosis [24,25]. Compared with current machine learning approaches for screening OSA risk, the current models have some advantages.…”

Section: Discussionmentioning

confidence: 99%

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Tsai

Huang

Cheng

et al. 2022

Sensors

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Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

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“…Another study used oximetry parameters during sleep and body profiles to differentiate between high- or low-ArTH OSA and insomnia. That study observed that patients with both high- and low-ArTH OSA had higher BMI and wider neck circumference and waist circumference compared with patients with insomnia [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution

Hsu

Yang

Tsai

et al. 2023

Life

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Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E–I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E–I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

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Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features

Cited by 9 publications

References 71 publications

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution

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