| Project/Area Number |
21K17670
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 59040:Nutrition science and health science-related
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| Research Institution | Kyoto University of Advanced Science |
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Principal Investigator |
梁 滋路 京都先端科学大学, 工学部, 講師 (10782807)
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| Project Period (FY) |
2021-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2024: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2023: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2022: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2021: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | sleep apnea / SpO2 / ensemble learning / machine learning / multiscale entropy / oximeter / wearable computing / ubiquitous computing / personal informatics / digital health / mHealth / chaos theory |
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| Outline of Research at the Start |
SAHS screening markers will be identified through nonlinear analysis of the chaotic dynamics of the physiological data measurable by wearable devices. These markers, together with features derived using data science techniques, will be used to develop SAHS screening algorithms.
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| 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.
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| 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.
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| 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.
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