Earticle

초록

영어

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG- derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient’s sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

목차

Abstract
1. Introduction
2. Related work
2.1 Medical Techniques for OSA Treatment
2.2 OSA Prediction in Sleep Data Using Machine Learning
3. Method
3.1 Data Acquisition
3.2 Study Population
3.3 Data Preprocessing
3.4 Modeling
4. Results and discussion
5. Conclusion
6. Acknowledgement
7. Ethics statement
8. Reference

키워드

저자

• Juhyeong Kang [ Master Student, Department of Artificial Intelligence Convergence, Ewha Womans University, Seoul, Korea ]
• Yeojin Kim [ Master Student, Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea ]
• Jiseon Yang [ Undergraduate Student, Department of Mathematics, Ewha Womans University, Seoul, Korea ]
• Seungwon Chung [ Resident, Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Korea ]
• Sungeun Hwang [ Clinical Assistant Professor, Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Korea ]
• Uran Oh [ Professor, Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea ]
• Hyang Woon Lee [ Professor, Departments of Neurology and Medical Science, College of Medicine and Ewha Medical Research Institute, Graduate Programs of Artificial Intelligence Convergence and System Health Science & Engineering, Ewha Womans University, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380