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Nonnegative matrix factorization and sparse representation for the automated detection of periodic limb movements in sleep

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Abstract

Stroke is a leading cause of death and disability in adults, and incurs a significant economic burden to society. Periodic limb movements (PLMs) in sleep are repetitive movements involving the great toe, ankle, and hip. Evolving evidence suggests that PLMs may be associated with high blood pressure and stroke, but this relationship remains underexplored. Several issues limit the study of PLMs including the need to manually score them, which is time-consuming and costly. For this reason, we developed a novel automated method for nocturnal PLM detection, which was shown to be correlated with (a) the manually scored PLM index on polysomnography, and (b) white matter hyperintensities on brain imaging, which have been demonstrated to be associated with PLMs. Our proposed algorithm consists of three main stages: (1) representing the signal in the time–frequency plane using time–frequency matrices (TFM), (2) applying K-nonnegative matrix factorization technique to decompose the TFM matrix into its significant components, and (3) applying kernel sparse representation for classification (KSRC) to the decomposed signal. Our approach was applied to a dataset that consisted of 65 subjects who underwent polysomnography. An overall classification of 97 % was achieved for discrimination of the aforementioned signals, demonstrating the potential of the presented method.

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Acknowledgments

The authors thank Mr. Dana Jewell for scoring all the overnight sleep studies. The authors also thank the staff of the Sunnybrook Health Sciences Centre sleep laboratory, sleep and stroke clinics, and the study participants for their assistance throughout the study. Dr. Boulos was supported by a Focus on Stroke 2010 Research Fellowship during this study, which was funded by the Heart and Stroke Foundation of Canada, the Canadian Stroke Network and the Canadian Institutes of Health Research. We also gratefully acknowledge the funding support of the Canada Research Chairs Program.

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Authors and Affiliations

  1. Department of Computer Science, Toronto Rehabilitation Institute, University of Toronto, 555 University Ave, Toronto, ON, M5G 2A2, Canada

    Mehrnaz Shokrollahi

  2. Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada

    Sridhar Krishnan & Dustin D. Dopsa

  3. Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre and University of Toronto, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada

    Ryan T. Muir, Sandra E. Black, Richard H. Swartz, Brian J. Murray & Mark I. Boulos

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  1. Mehrnaz Shokrollahi
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  4. Ryan T. Muir
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Correspondence to Mehrnaz Shokrollahi.

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Additional information

This study population was derived from two trials that were registered on clinicaltrials.gov: 1. Post-stroke Triage “DOC”: Simple Screening for Depression, Obstructive Sleep Apnea and Cognitive Impairment NCT02007265 2. Sleep Disorders Managed and Assessed Rapidly in Transient Ischemic Attack (TIA) and In Early Stroke (SMARTIES) NCT01528462.

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Shokrollahi, M., Krishnan, S., Dopsa, D.D. et al. Nonnegative matrix factorization and sparse representation for the automated detection of periodic limb movements in sleep. Med Biol Eng Comput 54, 1641–1654 (2016). https://doi.org/10.1007/s11517-015-1444-y

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