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Signal processing in prolonged EEG recordings during intensive care

Authors :: S. White
Carsten E. Thomsen
K. Jensen
P.F. Prior
M. Van Gils
J. Gade
Lotfi Senhadji
R.M. Longford
Annelise Rosenfalck
I.R. Ghosh
VTT Information technology
Technical Research Centre of Finland
Departement of medical informatics and image analysis
Aalborg University [Denmark] (AAU)
Departement of clinical neurophysiology
St Bartolomew's Hospital
Judex Datasystemer A/S
Laboratoire Traitement du Signal et de l'Image (LTSI)
Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Departement of oral function and physiology
University of Copenhagen = Københavns Universitet (UCPH)
Anaesthetics laboratory
Departement of anaesthesiology
Hjorring hospital
Université de Rennes 1 (UR1)
Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)
University of Copenhagen = Københavns Universitet (KU)
Senhadji, Lotfi
Source :: van Gils, M, Rosenfalck, A, White, S, Prior, P, Grade, J, Senhadji, L, Thomsen, C, Ghosh, R, Langford, R & Kjeld, J 1997, ' Signal processing in prolonged EEG recordings during intensive care ', IEEE Engineering in Medicine and Biology Magazine, vol. 16, no. 6, pp. 56-63 . https://doi.org/10.1109/51.637118, IEEE Engineering in Medicine and Biology Magazine, IEEE Engineering in Medicine and Biology Magazine, 1997, 16 (6), pp.56-63. ⟨10.1109/51.637118⟩, IEEE Engineering in Medicine and Biology Magazine, Institute of Electrical and Electronics Engineers, 1997, 16 (6), pp.56-63. ⟨10.1109/51.637118⟩
Publication Year :: 1997
Publisher :: Institute of Electrical and Electronics Engineers (IEEE), 1997.
Abstract: International audience; Methods for analyzing and displaying EEG signals are discussed. The increasing availability and affordability of powerful computer equipment makes possible the use of ever more sophisticated signal processing techniques, which extract relevant (but not readily discernible) information from long-term EEG recordings and can easily identify important features in the EEG. Whether these techniques are actually taken up in clinical practice is heavily dependent on how well they match clinical requirements. This article concentrates on requirements set in the context of long-term recordings in the ICU that demand the ability to process short-term discrete events as well as long-term trend information. A huge range of potentially useful signal processing techniques exists. This article illustrates the value of some of these techniques for ICU signals using the EEG recordings collected during the IMPROVE project