Nonlinear analysis of the electroencephalogram in depth of anesthesia
Digital signal processing of the electroencephalogram (EEG) became important in monitoring depth of anesthesia (DoA) being used to provide a better anesthetic technique. The objective of this work was to conduct a review about nonlinear mathematical methods applied recently to the analyses of nonli...
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| Format: | Article |
| Language: | English |
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Universidad de Antioquia
2015-05-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
| Subjects: | |
| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/17958 |
| _version_ | 1797861740424200192 |
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| author | Oscar Leonardo Mosquera-Dusan Daniel Alfonso Botero-Rosas Mauricio Cagy Ruben Dario Henao-Idarraga |
| author_facet | Oscar Leonardo Mosquera-Dusan Daniel Alfonso Botero-Rosas Mauricio Cagy Ruben Dario Henao-Idarraga |
| author_sort | Oscar Leonardo Mosquera-Dusan |
| collection | DOAJ |
| description |
Digital signal processing of the electroencephalogram (EEG) became important in monitoring depth of anesthesia (DoA) being used to provide a better anesthetic technique. The objective of this work was to conduct a review about nonlinear mathematical methods applied recently to the analyses of nonlinear non-stationary EEG signal. A review was conducted showing time- and frequency-domain nonlinear mathematical methods recently applied to EEG analysis: Approximate Entropy, Sample Entropy, Spectral Entropy, Permutation Entropy, Wavelet Transform, Wavelet Entropy, Bispectrum, Bicoherence and Hilbert Huang Transform. Some algorithms were implemented and tested in one EEG signal record from a patient at The Sabana University Clinic. Recently published results from different methods are discussed. Nonlinear techniques such as entropy analysis in time domain and combination with wavelet transform, and Hilbert Huang transform in frequency domain have shown promising results in classifications of depth of anesthesia stages.
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| first_indexed | 2024-04-09T22:09:05Z |
| format | Article |
| id | doaj.art-21542c896b5b41a6ab14c2592f5b9f8f |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-09T22:09:05Z |
| publishDate | 2015-05-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-21542c896b5b41a6ab14c2592f5b9f8f2023-03-23T12:31:13ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442015-05-017510.17533/udea.redin.n75a06Nonlinear analysis of the electroencephalogram in depth of anesthesiaOscar Leonardo Mosquera-Dusan0Daniel Alfonso Botero-Rosas1Mauricio Cagy2Ruben Dario Henao-Idarraga3Savannah CollegeSavannah CollegeFederal University of Rio de JaneiroClinic University of La Sabana Digital signal processing of the electroencephalogram (EEG) became important in monitoring depth of anesthesia (DoA) being used to provide a better anesthetic technique. The objective of this work was to conduct a review about nonlinear mathematical methods applied recently to the analyses of nonlinear non-stationary EEG signal. A review was conducted showing time- and frequency-domain nonlinear mathematical methods recently applied to EEG analysis: Approximate Entropy, Sample Entropy, Spectral Entropy, Permutation Entropy, Wavelet Transform, Wavelet Entropy, Bispectrum, Bicoherence and Hilbert Huang Transform. Some algorithms were implemented and tested in one EEG signal record from a patient at The Sabana University Clinic. Recently published results from different methods are discussed. Nonlinear techniques such as entropy analysis in time domain and combination with wavelet transform, and Hilbert Huang transform in frequency domain have shown promising results in classifications of depth of anesthesia stages. https://revistas.udea.edu.co/index.php/ingenieria/article/view/17958EEG features extractionnonlinear complexity analysesdigital signal processingdepth of anesthesia monitoring |
| spellingShingle | Oscar Leonardo Mosquera-Dusan Daniel Alfonso Botero-Rosas Mauricio Cagy Ruben Dario Henao-Idarraga Nonlinear analysis of the electroencephalogram in depth of anesthesia Revista Facultad de Ingeniería Universidad de Antioquia EEG features extraction nonlinear complexity analyses digital signal processing depth of anesthesia monitoring |
| title | Nonlinear analysis of the electroencephalogram in depth of anesthesia |
| title_full | Nonlinear analysis of the electroencephalogram in depth of anesthesia |
| title_fullStr | Nonlinear analysis of the electroencephalogram in depth of anesthesia |
| title_full_unstemmed | Nonlinear analysis of the electroencephalogram in depth of anesthesia |
| title_short | Nonlinear analysis of the electroencephalogram in depth of anesthesia |
| title_sort | nonlinear analysis of the electroencephalogram in depth of anesthesia |
| topic | EEG features extraction nonlinear complexity analyses digital signal processing depth of anesthesia monitoring |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/17958 |
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