title: [Reduction of the number of recorded EEG channels for routine monitoring in the intensive care unit].
abstract: Monitoring patients in the intensive care unit with the aid of the conventional electroencephalogram employing a large number of recording channels is rather difficult, and can be laborious. This imposes limits on the routine application of this method. To investigate the possibility of developing a new monitoring device for easier application in the ICU, we aimed to establish whether the relevant information provided by a multi-channel EEG could be found in a subgroup of channels, thus reducing the number of channels required. Preferably those channels should be identified for use which are least contaminated by artefacts under routine conditions in the ICU. A total of 150 EEG recordings from the intensive care unit were inspected visually for the presence of artefacts. The derivations C3-P3 and C4-P4 proved to be least contaminated, at 35% and 39%, respectively. In these derivations visual assessment of the EEG was found to be impossible due to artefacts in only 4 and 5%, of all cases, respectively. A data set comprising 52 EEG segments with the fewest possible artefacts, was analysed using time series methods. On the basis of multivariate autoregressive processes, a measure was derived which describes the loss of information associated with a reduction in the number of EEG channels. The computation of the information loss for several channel combinations revealed that the derivations F3-C3, C3-P3 and A1-Cz represent a good compromise between information content, number of channels and frequency of artefacts. Practical experience shows that, at least for the control of sedation, a further reduction to a single channel should be possible.(ABSTRACT TRUNCATED AT 250 WORDS)
authors.affiliation: Abteilung IV im Krankenhaus Oststadt, Medizinische Hochschule Hannover.
authors.forename: B
authors.initials: B
authors.lastname: Schultz
authors.forename: R
authors.initials: R
authors.lastname: Bender
authors.forename: A
authors.initials: A
authors.lastname: Schultz
authors.forename: I
authors.initials: I
authors.lastname: Pichlmayr
mesh_terms: D016477:Artifacts; D002540:Cerebral Cortex / Q000503:physiopathology; D004569:Electroencephalography / Q000295:instrumentation*; D006801:Humans; D007362:Intensive Care Units; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D015999:Multivariate Analysis; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*