2023 Fiscal Year Research-status Report
New ubiquitous computing system uniting chaos theory and data science for sleep apnea hypopnea syndrome (SAHS) screening with wearable devices
| Project/Area Number |
21K17670
|
|---|
| Research Institution | Kyoto University of Advanced Science |
|---|
Principal Investigator |
梁 滋路 京都先端科学大学, 工学部, 講師 (10782807)
|
|---|
| Project Period (FY) |
2021-04-01 – 2025-03-31
|
| Keywords | sleep apnea / SpO2 / ensemble learning / machine learning |
|---|
| Outline of Annual Research Achievements |
The achievement this year revolved around enhancing the performance of SpO2-based sleep apnea screening models. A new feature set was constructed leveraging machine learning and chaos analysis techniques. Probabilistic ensemble approach was applied to construct apnea screening models at three cutoff points: 5, 15, 30 events/h. These models underwent thorough evaluation using multiple performance metrics and rigorous statistical analysis. The impact of decision boundaries and data granularity were systematically explored, marking the first comprehensive investigation of its kind in machine learning based sleep apnea screening. The models outperform existing models by a substantial margin. The findings have led to several publications in international journals and conferences, indexed in Web of Science and Scopus.
|
| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The project progressed as planned during this fiscal year. A new feature set was constructed by combining classic machine learning and chaos analysis techniques. Utilizing this feature set, probabilistic ensemble models were constructed using three tuned and calibrated base classifiers: SVM, logistic regression, and light gradient boosting machine. Model performance was evaluated using multiple measures, considering varying decision boundaries and data granularity. The developed models demonstrated superior performance across all three AHI cutoffs compared to existing sleep apnea screening models. The new approach allows for elegant integration of the pre-test sleep apnea prevalence into model tuning, thereby enhancing its clinical relevance and applicability in real-world scenarios.
|
| Strategy for Future Research Activity |
We have identified two key directions for future research. Firstly, we plan to explore the effectiveness of an original feature engineering method, which involves multiscale feature extraction. This approach aims to capture more nuanced patterns and relationships within the data, potentially enhancing the performance of our sleep apnea screening models. Secondly, we intend to conduct a comprehensive assessment of the external validity and generalizability of the developed sleep apnea screening models. This evaluation will involve testing the models on other datasets to ensure their robustness and applicability across different contexts. By addressing these areas in future work, we aim to further advance the effectiveness and reliability of our models in real-world settings.
|
| Causes of Carryover |
The review cycle of our journal submission took longer than anticipated, resulting in a delay in payment of the planned publication fee until the next fiscal year.
|
Research Products
(20 results)
