EOG compression in polysomnographic recordings based on the Lempel-Ziv-Welch algorithm
•To compress large electrooculograms (EOGs) for polysomnographic recordings.•To show the performance of the Lempel-Ziv-Welch compression algorithm on EOGs.•To compare the performance of lossless and lossy compression techniques on EOGs. Nocturnal polysomnography (PSG) is a neurophysiological techniq...
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| Published in | Biomedical signal processing and control Vol. 103; p. 107372 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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01.05.2025
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| Abstract | •To compress large electrooculograms (EOGs) for polysomnographic recordings.•To show the performance of the Lempel-Ziv-Welch compression algorithm on EOGs.•To compare the performance of lossless and lossy compression techniques on EOGs.
Nocturnal polysomnography (PSG) is a neurophysiological technique that studies sleep by recording multiple physiological parameters. One is the electrical signal, called the electrooculogram (EOG), generated from eye movement. An extensive PSG signal recording, typically around 8 h, requires a massive volume of data to be transmitted and stored; compression is therefore required. This study aims to compress EOG signals effectively, providing high-quality reconstruction with low bit rates and acceptable distortions. The Sleep Disorders Research Center dataset is employed to verify the applicability of the devised method. The solution is founded on the Lempel-Ziv-Welch (LZW) algorithm, developed with MATLAB software. The signal is compressed using this algorithm and subsequently reconstructed. The algorithm’s performance is evaluated using five parameters: compression performance (CP), L2 energy retained in the compressed signal, percent root-mean-square difference (PRD) in the reconstruction, compressed signal size, and runtime. The findings of the experiment, which used 22 EOGs of different subjects, comprising 11 individuals with psychophysiological insomnia and 11 individuals without this condition, demonstrated that the LZW algorithm produced an average CP of 84.65% while retaining almost 100.44% of the signal energy and a PRD of 6.20% in the reconstructed signal. |
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| AbstractList | •To compress large electrooculograms (EOGs) for polysomnographic recordings.•To show the performance of the Lempel-Ziv-Welch compression algorithm on EOGs.•To compare the performance of lossless and lossy compression techniques on EOGs.
Nocturnal polysomnography (PSG) is a neurophysiological technique that studies sleep by recording multiple physiological parameters. One is the electrical signal, called the electrooculogram (EOG), generated from eye movement. An extensive PSG signal recording, typically around 8 h, requires a massive volume of data to be transmitted and stored; compression is therefore required. This study aims to compress EOG signals effectively, providing high-quality reconstruction with low bit rates and acceptable distortions. The Sleep Disorders Research Center dataset is employed to verify the applicability of the devised method. The solution is founded on the Lempel-Ziv-Welch (LZW) algorithm, developed with MATLAB software. The signal is compressed using this algorithm and subsequently reconstructed. The algorithm’s performance is evaluated using five parameters: compression performance (CP), L2 energy retained in the compressed signal, percent root-mean-square difference (PRD) in the reconstruction, compressed signal size, and runtime. The findings of the experiment, which used 22 EOGs of different subjects, comprising 11 individuals with psychophysiological insomnia and 11 individuals without this condition, demonstrated that the LZW algorithm produced an average CP of 84.65% while retaining almost 100.44% of the signal energy and a PRD of 6.20% in the reconstructed signal. |
| ArticleNumber | 107372 |
| Author | Ferrero, Francisco López, Alberto Qaisar, Saeed Mian Álvarez, Juan Carlos García, Gonzalo |
| Author_xml | – sequence: 1 givenname: Alberto orcidid: 0000-0003-3572-7632 surname: López fullname: López, Alberto email: uo181549@uniovi.es organization: Department of Electrical, Electronic, Communications and Systems Engineering, University of Oviedo, 33204 Gijón, Spain – sequence: 2 givenname: Gonzalo orcidid: 0009-0000-0920-3099 surname: García fullname: García, Gonzalo organization: Department of Electrical, Electronic, Communications and Systems Engineering, University of Oviedo, 33204 Gijón, Spain – sequence: 3 givenname: Saeed Mian orcidid: 0000-0002-4268-3482 surname: Qaisar fullname: Qaisar, Saeed Mian organization: Electrical and Computer Engineering Department, Effat University, 21478 Jeddah, Saudi Arabia – sequence: 4 givenname: Francisco orcidid: 0000-0003-3551-8160 surname: Ferrero fullname: Ferrero, Francisco organization: Department of Electrical, Electronic, Communications and Systems Engineering, University of Oviedo, 33204 Gijón, Spain – sequence: 5 givenname: Juan Carlos surname: Álvarez fullname: Álvarez, Juan Carlos organization: Department of Electrical, Electronic, Communications and Systems Engineering, University of Oviedo, 33204 Gijón, Spain |
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| Cites_doi | 10.1109/ACCESS.2020.2986397 10.1109/NTMS.2015.7266469 10.1164/ajrccm.162.3.9908002 10.1109/TENCON54134.2021.9707201 10.1109/MC.1984.1659158 10.1007/s40031-021-00686-3 10.1378/chest.124.4.1543 10.1109/I2MTC50364.2021.9459880 10.1109/I2MTC60896.2024.10560680 10.1109/ACCESS.2020.3023915 10.1109/NGCT.2015.7375242 10.1111/coa.12549 10.1109/TBCAS.2018.2824659 10.1109/TIT.1977.1055714 10.1016/j.dib.2017.09.033 10.4018/jalr.2011070102 10.1371/journal.pone.0218948 10.1007/978-3-540-68017-8_111 10.1016/j.compeleceng.2019.106462 10.1109/WESCAN.1991.160551 10.1109/DATE.2003.1253596 |
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| Keywords | Compression performance Root-mean-square difference Electrooculography L2 energy retained Polysomnography Lempel-Ziv-Welch algorithm |
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| Title | EOG compression in polysomnographic recordings based on the Lempel-Ziv-Welch algorithm |
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