Wireless Wearable Multisensory Suite and Real-Time Prediction of Obstructive Sleep Apnea Episodes

Obstructive sleep apnea (OSA) is a common sleep disorder found in 24% of adult men and 9% of adult women. Although continuous positive airway pressure (CPAP) has emerged as a standard therapy for OSA, a majority of patients are not tolerant to this treatment, largely because of the uncomfortable nasal air delivery during their sleep. Recent advances in wireless communication and advanced (“bigdata”) preditive analytics technologies offer radically new point-of-care treatment approaches for OSA episodes with unprecedented comfort and afforadability. We introduce a Dirichlet process-based mixture Gaussian process (DPMG) model to predict the onset of sleep apnea episodes based on analyzing complex cardiorespiratory signals gathered from a custom-designed wireless wearable multisensory suite. Extensive testing with signals from the multisensory suite as well as PhysioNet's OSA database suggests that the accuracy of offline OSA classification is 88%, and accuracy for predicting an OSA episode 1-min ahead is 83% and 3-min ahead is 77%. Such accurate prediction of an impending OSA episode can be used to adaptively adjust CPAP airflow (toward improving the patient's adherence) or the torso posture (e.g., minor chin adjustments to maintain steady levels of the airflow).

[1]  Paul Dagum,et al.  Time series prediction using belief network models , 1995, Int. J. Hum. Comput. Stud..

[2]  R. Grunstein,et al.  Adherence to Continuous Positive Airway Pressure Therapy , 2012 .

[3]  W. Randerath,et al.  Comparison of manual titration and automatic titration based on forced oscillation technique, flow and snoring in obstructive sleep apnea. , 2009, Sleep medicine.

[4]  R. Rogers,et al.  Patient compliance with nasal CPAP therapy for sleep apnea. , 1986, Chest.

[5]  Satish T S Bukkapatnam,et al.  Forecasting the evolution of nonlinear and nonstationary systems using recurrence-based local Gaussian process models. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Michael E Phelps,et al.  Systems Biology and New Technologies Enable Predictive and Preventative Medicine , 2004, Science.

[7]  A. Murray,et al.  Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings , 2002, Medical and Biological Engineering and Computing.

[8]  John R. Shambroom,et al.  Validation of an automated wireless system to monitor sleep in healthy adults , 2012, Journal of sleep research.

[9]  D. Gough,et al.  Toward prediction of physiological state signals in sleep apnea , 1998, IEEE Transactions on Biomedical Engineering.

[10]  S. Bukkapatnam.  Willingness-to-pay Prediction Based on Empirical Mode Decomposition , 2011 .

[11]  B. A. Phillips.  Long-term Cardiovascular Outcomes in Men With Obstructive Sleep Apnoea-Hypopnoea With or Without Treatment With Continuous Positive Airway Pressure: An Observational Study , 2007 .

[12]  J. B. Gao,et al.  Recurrence Time Statistics for Chaotic Systems and Their Applications , 1999 .

[13]  W. McNicholas,et al.  Long-term effects of nasal continuous positive airway pressure therapy on cardiovascular outcomes in sleep apnea syndrome. , 2005, Chest.

[14]  Ronald R Grunstein,et al.  Adherence to continuous positive airway pressure therapy: the challenge to effective treatment. , 2008, Proceedings of the American Thoracic Society.

[15]  Amparo Alonso-Betanzos,et al.  A new method for sleep apnea classification using wavelets and feedforward neural networks , 2005, Artif. Intell. Medicine.

[16]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[17]  T. Young,et al.  The occurrence of sleep-disordered breathing among middle-aged adults. , 1993, The New England journal of medicine.

[18]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[19]  G. Richards,et al.  Effects of humidification on nasal symptoms and compliance in sleep apnea patients using continuous positive airway pressure. , 1999, Chest.

[20]  Philippe Faure,et al.  A new method to estimate the Kolmogorov entropy from recurrence plots: its application to neuronal signals , 1998 .

[21]  S. Bukkapatnam,et al.  Prediction of sleep apnea episodes from a wireless wearable multisensor suite , 2013, 2013 IEEE Point-of-Care Healthcare Technologies (PHT).

[22]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[23]  P. Lavie,et al.  All-cause mortality in males with sleep apnoea syndrome: declining mortality rates with age , 2005, European Respiratory Journal.

[24]  Ralph Snyderman,et al.  Prospective care: a personalized, preventative approach to medicine. , 2006, Pharmacogenomics.

[25]  L. Hood,et al.  Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine , 2012, Journal of internal medicine.

[26]  S. Cerutti,et al.  Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[28]  C. Pollak,et al.  Quantitative analysis of sleep and sleep apnea before and after tracheostomy in patients with the hypersomnia-sleep apnea syndrome. , 1980, Sleep.

[29]  J. A. Stewart,et al.  Nonlinear Time Series Analysis , 2015 .

[30]  R. Berry,et al.  The use of auto-titrating continuous positive airway pressure for treatment of adult obstructive sleep apnea. An American Academy of Sleep Medicine review. , 2002, Sleep.

[31]  Michael I. Jordan,et al.  Variational inference for Dirichlet process mixtures , 2006 .

[32]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[33]  Satish T. S. Bukkapatnam,et al.  Topology and Random-Walk Network Representation of Cardiac Dynamics for Localization of Myocardial Infarction , 2013, IEEE Transactions on Biomedical Engineering.