Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness

V. Gerla, V. Kremen, Naima Covassin, L. Lhotska, E. A. Saifutdinova, J. Bukartyk, V. Marik, Virend Somers

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

A set of computationally inexpensive methods for reliable and robust detection of undesired signals in the EEG and EOG was designed, implemented, and tested. This strategy includes detection of eye blinking, eye movements, muscle activity, and flat lines in multichannel EEG and EOG data. The proposed methodology was verified on real awake data acquired in controlled conditions (44 recordings of total length 26.38 h) during Maintenance of Wakefulness Tests (MWT). The algorithms worked reliably (average precision was 0.992 ± 0.006, accuracy 0.988 ± 0.006, sensitivity 0.985 ± 0.009, and F1 score 0.988 ± 0.006) and fast (1 h of recording processed in 46.2 ± 5.3 s). We suggest testing this versatile and fast methodology on other type of EEG recordings with modification of threshold parameters if needed. This article reports data from a clinical trials no. NCT01433315 and NCT01580761.

Original languageEnglish (US)
Pages (from-to)381-390
Number of pages10
JournalBiomedical Signal Processing and Control
Volume31
DOIs
StatePublished - Jan 1 2017

Keywords

  • Artifacts
  • Automatic
  • Detection
  • EEG
  • Identification
  • Wakefulness

ASJC Scopus subject areas

  • Health Informatics
  • Signal Processing

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