From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters
Objective Current adjustments of continuous positive airway pressure (CPAP) may expose patients to risks of respiratory episodes or oxygen desaturation. Therefore, this study developed machine learning models using leading indicators, such as heart rate variability (HRV) and oximetry-related metrics...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251339273 |
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| author | Chih-Fan Kuo Yi-Chih Lin Ze-Yu Chen Jiunn-Horng Kang Cheng-Chen Chang Zhihe Chen Arnab Majumdar Yen-Ling Chen Yi-Chun Kuan Kang-Yun Lee Po-Hao Feng Kuan-Yuan Chen Hsin-Chien Lee Wun-Hao Cheng Wen-Te Liu Cheng-Yu Tsai |
| author_facet | Chih-Fan Kuo Yi-Chih Lin Ze-Yu Chen Jiunn-Horng Kang Cheng-Chen Chang Zhihe Chen Arnab Majumdar Yen-Ling Chen Yi-Chun Kuan Kang-Yun Lee Po-Hao Feng Kuan-Yuan Chen Hsin-Chien Lee Wun-Hao Cheng Wen-Te Liu Cheng-Yu Tsai |
| author_sort | Chih-Fan Kuo |
| collection | DOAJ |
| description | Objective Current adjustments of continuous positive airway pressure (CPAP) may expose patients to risks of respiratory episodes or oxygen desaturation. Therefore, this study developed machine learning models using leading indicators, such as heart rate variability (HRV) and oximetry-related metrics, to proactively predict optimal adjustment timings. Methods CPAP titration data were first collected from a sleep center in northern Taiwan. Subsequently, Continuous HRV and oximetry-related metrics were retrieved (60-s window with 1-s stride) and then labeled based on the presence of pressure adjustments. The dataset, comprising seven HRV and two oximetry-related parameters, was independently divided into training/validation (80%) and test (20%) datasets based on patient information. Five cross-sectional and two time-series models were established. The model with the highest accuracy and area under the receiver operating characteristic curve (AUROC) in the training/validation dataset was applied to the test dataset to investigate feature importance through permutation analysis. Results A dataset comprising 14,629 time-series cases from 374 patients undergoing CPAP therapy was obtained. The InceptionTime model outperformed others during the training/validation phase with accuracies of 78.27% and AUROC of 78.07%, and achieved 80.92% accuracy and 76.52% AUROC in the test phase. Feature importance analysis identified peripheral arterial oxygen saturation, its standard deviation over a 60-s window, and normalized power in the very-low-frequency band of HRV as the most impactful predictors. Conclusions The findings demonstrated the feasibility of incorporating HRV and oximetry-related metrics, as leading indicators, to proactively predict CPAP adjustment timings. Further research should consider integrating these metrics to improve CPAP therapy. |
| format | Article |
| id | doaj-art-902c6bfff6f4441c906ef9a0e0cd080a |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-902c6bfff6f4441c906ef9a0e0cd080a2025-08-20T03:11:26ZengSAGE PublishingDigital Health2055-20762025-04-011110.1177/20552076251339273From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parametersChih-Fan Kuo0Yi-Chih Lin1Ze-Yu Chen2Jiunn-Horng Kang3Cheng-Chen Chang4Zhihe Chen5Arnab Majumdar6Yen-Ling Chen7Yi-Chun Kuan8Kang-Yun Lee9Po-Hao Feng10Kuan-Yuan Chen11Hsin-Chien Lee12Wun-Hao Cheng13Wen-Te Liu14Cheng-Yu Tsai15 Artificial Intelligence Center, , Taichung, Taiwan Department of Otolaryngology, , New Taipei City, Taiwan TMU Research Center of Artificial Intelligence in Medicine and Health, , Taipei, Taiwan College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan School of Medicine, , Taichung, Taiwan Department of Civil and Environmental Engineering, , London, UK Department of Civil and Environmental Engineering, , London, UK College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan Department of Neurology, School of Medicine, College of Medicine, , Taipei City, Taiwan Respiratory Therapy Room, Division of Pulmonary Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan Respiratory Therapy Room, Division of Pulmonary Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan Department of Psychiatry, , Taipei, Taiwan School of Respiratory Therapy, College of Medicine, , Taipei, Taiwan Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, TaiwanObjective Current adjustments of continuous positive airway pressure (CPAP) may expose patients to risks of respiratory episodes or oxygen desaturation. Therefore, this study developed machine learning models using leading indicators, such as heart rate variability (HRV) and oximetry-related metrics, to proactively predict optimal adjustment timings. Methods CPAP titration data were first collected from a sleep center in northern Taiwan. Subsequently, Continuous HRV and oximetry-related metrics were retrieved (60-s window with 1-s stride) and then labeled based on the presence of pressure adjustments. The dataset, comprising seven HRV and two oximetry-related parameters, was independently divided into training/validation (80%) and test (20%) datasets based on patient information. Five cross-sectional and two time-series models were established. The model with the highest accuracy and area under the receiver operating characteristic curve (AUROC) in the training/validation dataset was applied to the test dataset to investigate feature importance through permutation analysis. Results A dataset comprising 14,629 time-series cases from 374 patients undergoing CPAP therapy was obtained. The InceptionTime model outperformed others during the training/validation phase with accuracies of 78.27% and AUROC of 78.07%, and achieved 80.92% accuracy and 76.52% AUROC in the test phase. Feature importance analysis identified peripheral arterial oxygen saturation, its standard deviation over a 60-s window, and normalized power in the very-low-frequency band of HRV as the most impactful predictors. Conclusions The findings demonstrated the feasibility of incorporating HRV and oximetry-related metrics, as leading indicators, to proactively predict CPAP adjustment timings. Further research should consider integrating these metrics to improve CPAP therapy.https://doi.org/10.1177/20552076251339273 |
| spellingShingle | Chih-Fan Kuo Yi-Chih Lin Ze-Yu Chen Jiunn-Horng Kang Cheng-Chen Chang Zhihe Chen Arnab Majumdar Yen-Ling Chen Yi-Chun Kuan Kang-Yun Lee Po-Hao Feng Kuan-Yuan Chen Hsin-Chien Lee Wun-Hao Cheng Wen-Te Liu Cheng-Yu Tsai From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters Digital Health |
| title | From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters |
| title_full | From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters |
| title_fullStr | From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters |
| title_full_unstemmed | From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters |
| title_short | From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters |
| title_sort | from reactive to proactive machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry related parameters |
| url | https://doi.org/10.1177/20552076251339273 |
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