Context related artefact detection in prolonged EEG recordings
| Autor: | Pjm Pierre Cluitmans, M Maarten van de Velde, IR Robert Ghosh |
|---|---|
| Přispěvatelé: | Signal Processing Systems, Medical signal processing |
| Jazyk: | angličtina |
| Rok vydání: | 1999 |
| Předmět: |
Adult
Male Matching (statistics) Speech recognition Health Informatics Context (language use) Electroencephalography Sensitivity and Specificity Eeg data Medicine Humans Set (psychology) Aged Observer Variation Signal processing Analysis of Variance Epilepsy Models Statistical medicine.diagnostic_test business.industry Middle Aged Computer Science Applications Data set body regions Autoregressive model Feasibility Studies Regression Analysis sense organs business Artifacts Software |
| Zdroj: | Computer Methods and Programs in Biomedicine, 60(3), 183-196. Elsevier Ireland Ltd |
| ISSN: | 0169-2607 |
| Popis: | The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. Although different EEG analysis systems have been described, only few general applicable artefact recognition techniques have emerged. This paper tackles the problem of artefact detection in seven 24 h EEG recordings in the intensive care unit. ICU recordings have received less attention than, e.g. epilepsy monitoring, although recordings in this environment present an interesting application area. The EEG data used here was recorded during the difficult circumstances of an explorative ICU study. The data set includes a diverse set of EEG patterns, as well as EEG artefacts. The study investigates objective artefact detection methods based on statistical differences between signal parameters, using time-varying autoregressive modelling (AR) and Slope detection. In addition to matching the performance of artefact detection against two human observers, the study focuses on the optimal settings for context incorporation by testing the algorithms for different time windows and epoch lengths. Results indicate that a relatively short period (20-40 s) provides sufficient context information for the methods used. The combined AR and Slope detection parameters yielded good performance, detecting approximately 90% of the artefacts as indicated by the consensus score of the human observers. |
| Databáze: | OpenAIRE |
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