Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets.
Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode on...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5105938?pdf=render |
| _version_ | 1818526590843224064 |
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| author | Trung Q Le Satish T S Bukkapatnam |
| author_facet | Trung Q Le Satish T S Bukkapatnam |
| author_sort | Trung Q Le |
| collection | DOAJ |
| description | Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders. |
| first_indexed | 2024-12-11T06:25:08Z |
| format | Article |
| id | doaj.art-b8788755b30c4ad8b476c13a82d58d26 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T06:25:08Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b8788755b30c4ad8b476c13a82d58d262022-12-22T01:17:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-011111e016440610.1371/journal.pone.0164406Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets.Trung Q LeSatish T S BukkapatnamRecent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders.http://europepmc.org/articles/PMC5105938?pdf=render |
| spellingShingle | Trung Q Le Satish T S Bukkapatnam Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets. PLoS ONE |
| title | Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets. |
| title_full | Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets. |
| title_fullStr | Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets. |
| title_full_unstemmed | Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets. |
| title_short | Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets. |
| title_sort | nonlinear dynamics forecasting of obstructive sleep apnea onsets |
| url | http://europepmc.org/articles/PMC5105938?pdf=render |
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