Enhancing sleep apnea diagnosis : evaluating wellue O2 ring and prediction models for wearable device-based detection | |
| Author | Thapa, Kristina |
| Call Number | AIT Thesis no.IM-23-03 |
| Subject(s) | Sleep apnea syndromes Wearable technology--Evaluation Polysomnography |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | Sleep apnea is a common sleep disorder that can have significant negative consequences, including cognitive disabilities, excessive sleepiness, and depression. However, it is often not diagnosed, which causes delays in treatment. Standard diagnostic procedures, polysomnography, are complex and expensive and require specialized facilities and personnel. To address this problem, various wearable devices have been proposed to diagnose sleep apnea at patients' homes. This study aimed to identify the most suitable model for sleep apnea detection using wearable devices. Pure Health LIFE HR 2 and Wellue O2 ring were the wearable devices used in this study. Additionally, polysomnography was used as the ground truth. In the study, patients were required to wear both devices, including polysomnography sensors, while sleeping at the Thammasat Hospital sleep lab. A custom application was used to collect SpO2 (oxygen saturation) data from the smartwatch. The study found that the Pure Health LIFE HR 2 was unable to read SpO2 below 95, while the O2 ring had good accuracy, with a deviation of 5% from the gold standard for SpO2 reading. The data collected from the O2 ring were then used for prediction purposes with a rule-based approach model. However, only this model, which considered an event in which SpO2 dropped below 3% for 10 seconds, achieved only 30% accuracy in predicting apnea. To improve precision, SVM (Support Vector Machine) and 1D CNN LSTM (Convolutionary Neural Network with Long-Term Memory) models were implemented, incorporating additional features such as motion, pulse rate, and patient demographic. The accuracy of the model improved implementing this parameter since only SpO2 data can be misleading to the model. Some apneic patient doesn’t have the oxygen desaturation. The 1D CNN LSTM model had overfitting issue due to the limited and unbalanced set of data containing only 129 patients. The overall accuracy of the 1D CNN LSTM model was 62%, but it tended to predict most cases as positive. Based on these challenges, SVM was chosen because it works well with smaller datasets like ours. The SVM model was found to be 88.8% accurate in detecting sleep apnea, a significant improvement compared to previous models. These results highlight the potential of the Wellue O2 ring as a promising device for the detection of sleep apnea, especially in areas where polysomnography facilities are not widely available. |
| Year | 2023 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Chutiporn Anutariya |
| Examination Committee(s) | Aekavute Sujarae;Chantri Polprasert |
| Scholarship Donor(s) | Asian Institute of Technology |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2023 |