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Improvement in the performance of automated spike detection using dipole source features for artefact rejection

Flanagan, D ; Agarwal, R ; Wang, Y.H ; Gotman, J

Clinical neurophysiology, 2003, Vol.114 (1), p.38-49 [Periódico revisado por pares]

Shannon: Elsevier Ireland Ltd

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  • Título:
    Improvement in the performance of automated spike detection using dipole source features for artefact rejection
  • Autor: Flanagan, D ; Agarwal, R ; Wang, Y.H ; Gotman, J
  • Assuntos: Artefact rejection ; Biological and medical sciences ; Dipole source ; Electrodiagnosis. Electric activity recording ; Electroencephalography - methods ; Epilepsy - physiopathology ; Humans ; Investigative techniques, diagnostic techniques (general aspects) ; Medical sciences ; Nervous system ; Signal Processing, Computer-Assisted ; Spike detection
  • É parte de: Clinical neurophysiology, 2003, Vol.114 (1), p.38-49
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Objective: We evaluated the use of an efficient dipole source algorithm to improve performance of automated spike detection by identifying false detections caused by artefacts. Methods: Automated spike detections were acquired from 26 patients undergoing prolonged electroencephalograph (EEG) monitoring. Data from 6 patients were used to develop the method and data from 20 patients were used to test the method. To provide a standard against which to evaluate the results, an electroencephalographer (EEGer) visually categorized all automated detections before the dipole models were calculated for all events. The event categories (as defined by the EEGer) were then combined with properties of the dipole model and features were identified that differentiated spike and artefact detections. The resulting method was then applied to the testing data set. Results: Residual variance and eccentricity of the dipole models differentiated artefact and spike detections. A separate set of rules defining eye blink artefact was also developed. The combined criteria removed a mean of 53.2% of artefact from the testing data set. Some spike detections (4.3%) were also lost. Conclusions: The features of the dipole source of a detected event can be used to differentiate artefacts from spikes. This algorithm is computationally light and could be implemented on-line.
  • Editor: Shannon: Elsevier Ireland Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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