Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings
EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of colle...
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todo:paper_01400118_v42_n4_p516_Rosso2023-10-03T14:58:12Z Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings Rosso, O.A. Figliola, A. Creso, J. Serrano, E. EEG Epileptic seizures Non-linear dynamics metric tools Wavelet analysis Muscle artifacts Physicians Physiological noise Acoustic noise Biomedical engineering Information analysis Muscle Signal detection Time series analysis Electroencephalography analytic method analytical parameters article artifact computer analysis controlled study correlation analysis electroencephalogram filtration frequency analysis metric system muscle contraction nonlinear system recording signal noise ratio time series analysis tonic clonic seizure Artifacts Electroencephalography Epilepsy, Tonic-Clonic Humans Signal Processing, Computer-Assisted EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of collected information. The aim of this work was to develop a computer-based method of time series analysis for such EEGs. A method is presented for filtering those frequencies associated with muscle activity using a wavelet transform. One of the advantages of this method over traditional filtering is that wavelet filtering of some frequency bands does not modify the pattern of the remaining ones. In consequence, the dynamics associated with them do not change. After generation of a 'noise free' signal by removal of the muscle artifacts using wavelets, a dynamic analysis was performed using non-linear dynamics metric tools. The characteristic parameters evaluated (correlation dimension D2 and largest Lyapunov exponent λ1) were compatible with those obtained in previous works. The average values obtained were: D2 = 4.25 and λ1=3.27 for the pre-ictal stage, D2=4.03 and λ1=2.68 for the tonic seizure stage, D2=4.11 and λ1=2.46 for the clonic seizure stage. © IFMBE: 2004. Fil:Figliola, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Serrano, E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01400118_v42_n4_p516_Rosso |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
EEG Epileptic seizures Non-linear dynamics metric tools Wavelet analysis Muscle artifacts Physicians Physiological noise Acoustic noise Biomedical engineering Information analysis Muscle Signal detection Time series analysis Electroencephalography analytic method analytical parameters article artifact computer analysis controlled study correlation analysis electroencephalogram filtration frequency analysis metric system muscle contraction nonlinear system recording signal noise ratio time series analysis tonic clonic seizure Artifacts Electroencephalography Epilepsy, Tonic-Clonic Humans Signal Processing, Computer-Assisted |
| spellingShingle |
EEG Epileptic seizures Non-linear dynamics metric tools Wavelet analysis Muscle artifacts Physicians Physiological noise Acoustic noise Biomedical engineering Information analysis Muscle Signal detection Time series analysis Electroencephalography analytic method analytical parameters article artifact computer analysis controlled study correlation analysis electroencephalogram filtration frequency analysis metric system muscle contraction nonlinear system recording signal noise ratio time series analysis tonic clonic seizure Artifacts Electroencephalography Epilepsy, Tonic-Clonic Humans Signal Processing, Computer-Assisted Rosso, O.A. Figliola, A. Creso, J. Serrano, E. Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings |
| topic_facet |
EEG Epileptic seizures Non-linear dynamics metric tools Wavelet analysis Muscle artifacts Physicians Physiological noise Acoustic noise Biomedical engineering Information analysis Muscle Signal detection Time series analysis Electroencephalography analytic method analytical parameters article artifact computer analysis controlled study correlation analysis electroencephalogram filtration frequency analysis metric system muscle contraction nonlinear system recording signal noise ratio time series analysis tonic clonic seizure Artifacts Electroencephalography Epilepsy, Tonic-Clonic Humans Signal Processing, Computer-Assisted |
| description |
EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of collected information. The aim of this work was to develop a computer-based method of time series analysis for such EEGs. A method is presented for filtering those frequencies associated with muscle activity using a wavelet transform. One of the advantages of this method over traditional filtering is that wavelet filtering of some frequency bands does not modify the pattern of the remaining ones. In consequence, the dynamics associated with them do not change. After generation of a 'noise free' signal by removal of the muscle artifacts using wavelets, a dynamic analysis was performed using non-linear dynamics metric tools. The characteristic parameters evaluated (correlation dimension D2 and largest Lyapunov exponent λ1) were compatible with those obtained in previous works. The average values obtained were: D2 = 4.25 and λ1=3.27 for the pre-ictal stage, D2=4.03 and λ1=2.68 for the tonic seizure stage, D2=4.11 and λ1=2.46 for the clonic seizure stage. © IFMBE: 2004. |
| format |
JOUR |
| author |
Rosso, O.A. Figliola, A. Creso, J. Serrano, E. |
| author_facet |
Rosso, O.A. Figliola, A. Creso, J. Serrano, E. |
| author_sort |
Rosso, O.A. |
| title |
Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings |
| title_short |
Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings |
| title_full |
Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings |
| title_fullStr |
Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings |
| title_full_unstemmed |
Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings |
| title_sort |
analysis of wavelet-filtered tonic-clonic electroencephalogram recordings |
| url |
http://hdl.handle.net/20.500.12110/paper_01400118_v42_n4_p516_Rosso |
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1807320843521359872 |