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Author Notes:

Correspondence: Donald L. Bliwise, Ph.D., Program in Sleep Medicine, Emory University School of Medicine, Wesley Woods Center, 1841 Clifton Road, Room 509, Atlanta, Georgia 30329; Phone: 1-404-728-4751; Fax: 1-404-728-4756; Email: dbliwis@emory.edu

Acknowledgments: We gratefully acknowledge the following individuals for their assistance in conducting this study: Sophia Greer, Shannon Hollars, Dr. David Rye, Dr. Lynn Marie Trotti, Ray Williams, and Anthony Wilson.

Subjects:

Research Funding:

This work was supported by 1 R01 NS-050595; 1 R01 NS-055015; 1 F32 NS-070572 and the “Action support post-doctoral fellows of the Operational Programme Education and Lifelong Learning” of the Greek Ministry of Education, Lifelong Learning and Religious Affairs, co-financed by the European Union.

Keywords:

  • Electromyography
  • Sleep
  • Muscle Activity
  • Phasic Activity
  • Polysomnography
  • Computer Detection

Computer detection approaches for the identification of phasic electromyographic (EMG) activity during human sleep

Tools:

Journal Title:

Biomedical Signal Processing and Control

Volume:

Volume 7, Number 6

Publisher:

, Pages 606-615

Type of Work:

Article | Post-print: After Peer Review

Abstract:

BACKGROUND Examination of spontaneously occurring phasic muscle activity from the human polysomnogram may have considerable clinical importance for patient care, yet most attempts to quantify the detection of such activity have relied upon laborious and intensive visual analyses. We describe in this study innovative signal processing approaches to this issue. METHODS We examined multiple features of surface electromyographic signals based on 16,200 individual 1-second intervals of low impedance sleep recordings. We validated which of those features most closely mirrored the careful judgments of trained human observers in making discriminations of the presence of short-lived (100-500 msec) phasic activity, and also examined which features provided maximal differences across 1-second intervals and which features were least susceptible to residual levels of amplifier noise. RESULTS Our data suggested particularly promising and novel features (e.g., Non-linear energy, 95th percentile of Spectral Edge Frequency) for developing automated systems for quantifying muscle activity during human sleep. CONCLUSIONS The EMG signals recorded from surface electrodes during sleep can be processed with techniques that reflect the visually based analyses of the human scorer but also offer potential for discerning far more subtle effects, Future studies will explore both the clinical utility of these techniques and their relative susceptibility to and/or independence from signal artifacts.

Copyright information:

© 2012 Elsevier Ltd. All rights reserved.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/).

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