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
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/1/50 |
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| author | Cheng-Yu Tsai Yi-Chun Kuan Wei-Han Hsu Yin-Tzu Lin Chia-Rung Hsu Kang Lo Wen-Hua Hsu Arnab Majumdar Yi-Shin Liu Shin-Mei Hsu Shu-Chuan Ho Wun-Hao Cheng Shang-Yang Lin Kang-Yun Lee Dean Wu Hsin-Chien Lee Cheng-Jung Wu Wen-Te Liu |
| author_facet | Cheng-Yu Tsai Yi-Chun Kuan Wei-Han Hsu Yin-Tzu Lin Chia-Rung Hsu Kang Lo Wen-Hua Hsu Arnab Majumdar Yi-Shin Liu Shin-Mei Hsu Shu-Chuan Ho Wun-Hao Cheng Shang-Yang Lin Kang-Yun Lee Dean Wu Hsin-Chien Lee Cheng-Jung Wu Wen-Te Liu |
| author_sort | Cheng-Yu Tsai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:39:10Z |
| format | Article |
| id | doaj.art-2edd629af31d4e4a803278453c99ecbd |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T01:39:10Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-2edd629af31d4e4a803278453c99ecbd2023-11-23T13:27:23ZengMDPI AGDiagnostics2075-44182021-12-011215010.3390/diagnostics12010050Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric FeaturesCheng-Yu Tsai0Yi-Chun Kuan1Wei-Han Hsu2Yin-Tzu Lin3Chia-Rung Hsu4Kang Lo5Wen-Hua Hsu6Arnab Majumdar7Yi-Shin Liu8Shin-Mei Hsu9Shu-Chuan Ho10Wun-Hao Cheng11Shang-Yang Lin12Kang-Yun Lee13Dean Wu14Hsin-Chien Lee15Cheng-Jung Wu16Wen-Te Liu17Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UKDepartment of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanSchool of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, TaiwanDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, TaiwanDepartment of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanSleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanMaster Program in Thoracic Medicine School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, TaiwanDepartment of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UKSchool of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, TaiwanSleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanSchool of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, TaiwanGraduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei 110301, TaiwanSchool of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei 110301, TaiwanDivision of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanDepartment of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanDepartment of Psychiatry, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanSleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanSleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei 235041, TaiwanInsomnia 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.https://www.mdpi.com/2075-4418/12/1/50insomnia disorderobstructive sleep apneain-laboratory polysomnographyrespiratory arousal thresholdrandom forest |
| spellingShingle | Cheng-Yu Tsai Yi-Chun Kuan Wei-Han Hsu Yin-Tzu Lin Chia-Rung Hsu Kang Lo Wen-Hua Hsu Arnab Majumdar Yi-Shin Liu Shin-Mei Hsu Shu-Chuan Ho Wun-Hao Cheng Shang-Yang Lin Kang-Yun Lee Dean Wu Hsin-Chien Lee Cheng-Jung Wu Wen-Te Liu 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 Diagnostics insomnia disorder obstructive sleep apnea in-laboratory polysomnography respiratory arousal threshold random forest |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | insomnia disorder obstructive sleep apnea in-laboratory polysomnography respiratory arousal threshold random forest |
| url | https://www.mdpi.com/2075-4418/12/1/50 |
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