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Real-time prediction of disordered breathing events in people with obstructive sleep apnea

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Abstract

Purpose

Conventional therapies for obstructive sleep apnea (OSA) are effective but suffer from poor patient adherence and may not fully alleviate major OSA-associated cardiovascular risk factors or improve certain aspects of quality of life. Predicting the onset of disordered breathing events in OSA patients may lead to improved strategies for treating OSA and inform our understanding of underlying disease mechanisms. In this work, we describe a deployable system capable of performing real-time predictions of sleep disordered breathing events in patients diagnosed with OSA, providing a novel approach for gaining insight into OSA pathophysiology, discovering population subgroups, and improving therapies.

Methods

LArge Memory STorage and Retrieval artificial neural networks with 864 different configurations were applied to polysomnogram records from 64 patients. Wavelet transforms, measures of entropy, and other statistics were applied to six physiological signals to provide network inputs. Approximate statistical tests were used to determine the best performing network for each patient. The most important predictors of disordered breathing events in OSA patients were determined by analyzing internal network parameters.

Results

The average optimized individual prediction sensitivity and specificity were 0.81 and 0.77, respectively. Predictions were better than random guessing for all OSA patients. Analysis of internal network parameters revealed a high degree of heterogeneity among disordered breathing event predictors and may reveal patient subgroups.

Conclusions

We report the first practical system to predict individual disordered breathing events in a heterogeneous group of patients diagnosed with OSA. The pattern of disordered breathing predictors suggests variable underlying pathophysiological mechanisms and highlights the need for an individualized approach to OSA diagnosis, therapy, and management.

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Acknowledgments

Funding of this study was provided by the BTG International, NIH Award F30 HL097403, and NIH Grant TL1RR029877. The funding sources had no role in data collection, analysis, or the decision to publish.

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Authors and Affiliations

  1. Medical Scientist Training Program, University of Illinois at Chicago, Chicago, IL, 60612, USA

    Jonathan A. Waxman

  2. Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, 60612, USA

    Jonathan A. Waxman & Daniel Graupe

  3. Center for Narcolepsy, Sleep, and Health Research, Colleges of Nursing and Medicine, University of Illinois at Chicago, 845 South Damen Ave., MC 802, Chicago, IL, 60612, USA

    Jonathan A. Waxman & David W. Carley

  4. Department of Neurology and Rehabilitation, College of Medicine, University of Illinois, Chicago, IL, 60612, USA

    Daniel Graupe

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  1. Jonathan A. Waxman
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Correspondence to Jonathan A. Waxman.

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Waxman, J.A., Graupe, D. & Carley, D.W. Real-time prediction of disordered breathing events in people with obstructive sleep apnea. Sleep Breath 19, 205–212 (2015). https://doi.org/10.1007/s11325-014-0993-x

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  • DOI: https://doi.org/10.1007/s11325-014-0993-x

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