Real-time subspace denoising of polysomnographic data
Article No.: 77, Pages 1 - 4
Abstract
Analysis of polysomnographic (PSG) biosignals, collected during sleep studies, is the current gold-standard for sleep disorder assessment. Motion and imperfect contact of the wired sensors attached to the human body, to acquire the data, can introduce noise and artifacts that can diminish the quality of the collected data. In this work we present a subspace denoising method that exploits the low-dimensionality of the acquired data, and is able to reduce the noise and increase the SNR ratio in real-time, resulting in improved data quality.
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Index Terms
- Real-time subspace denoising of polysomnographic data
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Published In
July 2015
526 pages
ISBN:9781450334525
DOI:10.1145/2769493
- Conference Chair:
- Fillia Makedon,
- Program Chairs:
- Gian-Luca Mariottini,
- Oliver Korn,
- Illias Maglogiannis,
- Vangelis Metsis
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Sponsors
- NSF: National Science Foundation
- University of Texas at Austin: University of Texas at Austin
- Univ. of Piraeus: University of Piraeus
- NCRS: Demokritos National Center for Scientific Research
- Ionian: Ionian University, GREECE
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 July 2015
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- Short-paper
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PETRA '15
Sponsor:
- NSF
- University of Texas at Austin
- Univ. of Piraeus
- NCRS
- Ionian
PETRA '15: 8th PErvasive Technologies Related to Assistive Environments
July 1 - 3, 2015
Corfu, Greece
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