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An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals
The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3926621/ https://www.ncbi.nlm.nih.gov/pubmed/24434840 http://dx.doi.org/10.3390/s140101474 |
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author | Fauvel, Simon Ward, Rabab K. |
author_facet | Fauvel, Simon Ward, Rabab K. |
author_sort | Fauvel, Simon |
collection | PubMed |
description | The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types. |
format | Online Article Text |
id | pubmed-3926621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-39266212014-02-18 An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals Fauvel, Simon Ward, Rabab K. Sensors (Basel) Article The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types. Molecular Diversity Preservation International (MDPI) 2014-01-15 /pmc/articles/PMC3926621/ /pubmed/24434840 http://dx.doi.org/10.3390/s140101474 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Fauvel, Simon Ward, Rabab K. An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals |
title | An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals |
title_full | An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals |
title_fullStr | An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals |
title_full_unstemmed | An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals |
title_short | An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals |
title_sort | energy efficient compressed sensing framework for the compression of electroencephalogram signals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3926621/ https://www.ncbi.nlm.nih.gov/pubmed/24434840 http://dx.doi.org/10.3390/s140101474 |
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