title: Dual adaptive filtering by optimal projection applied to filter muscle artifacts on EEG and comparative study.
abstract: 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.
authors.affiliation: Faculté de Médecine et Maïeutique, University Catholic of Lille, 59000 Lille, France ; Unité de Traitement de Signaux Biomédicaux, 59000 Lille, France.
authors.forename: Samuel
authors.identifier: 0000-0002-5289-8591
authors.initials: S
authors.lastname: Boudet
authors.affiliation: Unité de Traitement de Signaux Biomédicaux, 59000 Lille, France ; Hautes Etudes d'Ingénieur, 13 Rue de Toul, 59000 Lille, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Peyrodie
authors.affiliation: Clinical Neurophysiology service of CHR of Lille, 59000 Lille, France.
authors.forename: William
authors.initials: W
authors.lastname: Szurhaj
authors.affiliation: Harvard Medical School, Boston, MA 02115, USA.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Bolo
authors.affiliation: I3MTO-EA 4708, Université d'Orléans, 45000 Orléans, France.
authors.forename: Antonio
authors.initials: A
authors.lastname: Pinti
authors.affiliation: Faculté de Médecine et Maïeutique, University Catholic of Lille, 59000 Lille, France ; Unité de Traitement de Signaux Biomédicaux, 59000 Lille, France ; Groupe Hospitalier de l'Institut Catholique Lillois, 59000 Lille, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Gallois
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D009132:Muscles / Q000503:physiopathology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted