Theoretical and Experimental Investigation of an Efficient SVD-based Near-lossless Compression Algorithm for Multichannel EEG Signals
In this paper, we investigate performance of a re-cently proposed near-lossless compression algorithm specifically devised for multichannel electroencephalograph (EEG) signals. The algorithm exploits the fact that singular value decomposition (SVD) is usually performed on EEG signals for denoising a...
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| Veröffentlicht in: | 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA) S. 1 - 6 |
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22.06.2022
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| Abstract | In this paper, we investigate performance of a re-cently proposed near-lossless compression algorithm specifically devised for multichannel electroencephalograph (EEG) signals. The algorithm exploits the fact that singular value decomposition (SVD) is usually performed on EEG signals for denoising and removing unwanted artifacts and that the same SVD can be used for compression purpose. In this paper, we derived an analytical expression for the expected compression ratio and an upper bound for the maximum distortion introduced by the algorithm after reconstruction. Moreover, performances of the algorithm have been investigated on an extended dataset containing real EEG signals related to subjects performing different sensorimotor tasks. Both analytical and experimental results reported in this paper show that the algorithm is able to attain a compression ratio proportional to the number of EEG channels by achieving a percentage root mean square distortion (PRD) in the order of 0.01 %. In particular, the achieved PRD is very low if compared with other state-of-the-art compression algorithms with similar complexity. Moreover, the algorithm allows the desired maximum absolute error to be fixed a priori. Therefore, we can consider this algorithm as an efficient tool for reducing the amount of memory necessary to record data and, at the same time, preserving actual clinical information of the signals besides compression. |
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| AbstractList | In this paper, we investigate performance of a re-cently proposed near-lossless compression algorithm specifically devised for multichannel electroencephalograph (EEG) signals. The algorithm exploits the fact that singular value decomposition (SVD) is usually performed on EEG signals for denoising and removing unwanted artifacts and that the same SVD can be used for compression purpose. In this paper, we derived an analytical expression for the expected compression ratio and an upper bound for the maximum distortion introduced by the algorithm after reconstruction. Moreover, performances of the algorithm have been investigated on an extended dataset containing real EEG signals related to subjects performing different sensorimotor tasks. Both analytical and experimental results reported in this paper show that the algorithm is able to attain a compression ratio proportional to the number of EEG channels by achieving a percentage root mean square distortion (PRD) in the order of 0.01 %. In particular, the achieved PRD is very low if compared with other state-of-the-art compression algorithms with similar complexity. Moreover, the algorithm allows the desired maximum absolute error to be fixed a priori. Therefore, we can consider this algorithm as an efficient tool for reducing the amount of memory necessary to record data and, at the same time, preserving actual clinical information of the signals besides compression. |
| Author | Campobello, Giuseppe Tatti, Elisa Segreto, Antonino Crupi, Giovanni Quartarone, Angelo Ghilardi, Maria Felice Donato, Nicola Quercia, Angelica Gugliandolo, Giovanni |
| Author_xml | – sequence: 1 givenname: Giuseppe surname: Campobello fullname: Campobello, Giuseppe email: giuseppe.campobello@unime.it organization: University of Messina,Department of Engineering,Messina,Italy – sequence: 2 givenname: Angelica surname: Quercia fullname: Quercia, Angelica email: angelica.quercia@unime.it organization: University of Messina,Department of BIOMORF,Messina,Italy – sequence: 3 givenname: Giovanni surname: Gugliandolo fullname: Gugliandolo, Giovanni email: giovanni.gugliandolo@unime.it organization: University of Messina,Department of Engineering,Messina,Italy – sequence: 4 givenname: Antonino surname: Segreto fullname: Segreto, Antonino email: asegreto@unime.it organization: University of Messina,Department of Engineering,Messina,Italy – sequence: 5 givenname: Elisa surname: Tatti fullname: Tatti, Elisa email: etatti@ccny.cuny.edu organization: CUNY School of Medicine, The City University of New York,New York,United States – sequence: 6 givenname: Maria Felice surname: Ghilardi fullname: Ghilardi, Maria Felice email: liceg@med.cuny.edu organization: CUNY School of Medicine, The City University of New York,New York,United States – sequence: 7 givenname: Giovanni surname: Crupi fullname: Crupi, Giovanni email: crupig@unime.it organization: University of Messina,Department of BIOMORF,Messina,Italy – sequence: 8 givenname: Angelo surname: Quartarone fullname: Quartarone, Angelo email: angelo.quartarone@unime.it organization: University of Messina,Department of BIOMORF,Messina,Italy – sequence: 9 givenname: Nicola surname: Donato fullname: Donato, Nicola email: nicola.donato@unime.it organization: University of Messina,Department of Engineering,Messina,Italy |
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| Snippet | In this paper, we investigate performance of a re-cently proposed near-lossless compression algorithm specifically devised for multichannel... |
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| SubjectTerms | biomedical signal processing Distortion electroencephalo-graph (EEG) Electroencephalography Memory management Near-lossless compression Noise reduction Signal processing algorithms singular value eecomposition (SVD) Task analysis Upper bound |
| Title | Theoretical and Experimental Investigation of an Efficient SVD-based Near-lossless Compression Algorithm for Multichannel EEG Signals |
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