NDLI: Non-negative matrix factorization and sparse representation for sleep signal classification

Content Provider	IEEE Xplore Digital Library
Author	Shokrollahi, M. Krishnan, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Ryerson Univ., Toronto, ON, Canada (Shokrollahi, M.; Krishnan, S.)
Abstract	Real-life signals such as biomedical signals are non-stationary and random in their pattern, and cannot be characterized by any specific waveform or spectral content. Processing of these natural signals involves consideration of certain significant attributes such as their non-stationary behavior over time, scaling behavior, translation invariance. Due to their random behavior, the existing discriminative methods often fail to provide a reasonable quantification performance, thereby resulting in poor classification rates. In order to address this issue, there exists a need for defining a suitable theoretical framework for biomedical signals. We have proposed, a robust Time-Frequency Nonnegative Matrix Factorization (TF-NMF) framework that uses sparse representation for quantification of sleep signals. This scheme incorporates a novel feature extraction algorithm. For signals that are nonstationary in nature, the degree of sparsity is lower compared to the stationary signals. This results into poor classification accuracy. However our proposed approach has proven that using NMF as input to the sparse representation for classification will improve the discrimination performance. Overall, maximum cross-validation performance of 87:9% was obtained, using the leave-one-out (LOO) approach for sleep abnormality detection using EMG signals. Although the computational complexity of the proposed algorithm might be higher compared to the other similar methods, this TF-NMF based method shows great potential for quantification and localization of time varying signals.
Starting Page	4318
Ending Page	4321
File Size	320172
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610501
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Matrix decomposition Vectors Feature extraction Sleep Sparse matrices Time-frequency analysis Accuracy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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