Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study
Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optim...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/374679 |
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| author | Samuel Boudet Laurent Peyrodie William Szurhaj Nicolas Bolo Antonio Pinti Philippe Gallois |
| author_facet | Samuel Boudet Laurent Peyrodie William Szurhaj Nicolas Bolo Antonio Pinti Philippe Gallois |
| author_sort | Samuel Boudet |
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| description | Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings. |
| format | Article |
| id | doaj-art-5d49fb5970084a38addef5befd4f6680 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-5d49fb5970084a38addef5befd4f66802025-08-20T02:05:01ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/374679374679Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative StudySamuel Boudet0Laurent Peyrodie1William Szurhaj2Nicolas Bolo3Antonio Pinti4Philippe Gallois5Faculté de Médecine et Maïeutique, University Catholic of Lille, 59000 Lille, FranceUnité de Traitement de Signaux Biomédicaux, 59000 Lille, FranceClinical Neurophysiology service of CHR of Lille, 59000 Lille, FranceHarvard Medical School, Boston, MA 02115, USAI3MTO-EA 4708, Université d’Orléans, 45000 Orléans, FranceFaculté de Médecine et Maïeutique, University Catholic of Lille, 59000 Lille, FranceMuscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.http://dx.doi.org/10.1155/2014/374679 |
| spellingShingle | Samuel Boudet Laurent Peyrodie William Szurhaj Nicolas Bolo Antonio Pinti Philippe Gallois Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study The Scientific World Journal |
| title | Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study |
| title_full | Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study |
| title_fullStr | Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study |
| title_full_unstemmed | Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study |
| title_short | Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study |
| title_sort | dual adaptive filtering by optimal projection applied to filter muscle artifacts on eeg and comparative study |
| url | http://dx.doi.org/10.1155/2014/374679 |
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