Automatic EEG analysis during long-term monitoring in the ICU.
To assist in the reviewing of prolonged EEGs, we have developed an automatic EEG analysis method that can be used to compress the prolonged EEG into two pages. The proposed approach of Automatic Analysis of Segmented-EEG (AAS-EEG) consists of 4 basic steps: (1) segmentation; (2) feature extraction; (3) classification; and (4) presentation. The idea is to break down the EEG into stationary segments and extract features that can be used to classify the segments into groups of like patterns. The final step involves the presentation of the processed data in a compressed form. This is done by providing the EEGer with a representative sample from each group of EEG patterns and a compressed time profile of the complete EEG. To verify the above approach, 41 6 h EEG records were assessed for normality via the AAS-EEG and conventional EEG approaches. The difference between the overall assessment via compressed and conventional EEG was within one abnormality level 100% of the time, and within one-half level for 73.6% of the records. We demonstrated the feasibility and reliability of automatically segmenting and clustering the EEG, thus allowing the reduction of a 6 h tracing to a few representative segments and their time sequence. This should facilitate review of long recordings during monitoring in the ICU.
Agarwal
R
R
Montreal Neurological Institute, McGill University, Quebec, Canada.
Gotman
J
J
Flanagan
D
D
Rosenblatt
B
B
D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography / Q000379:methods*; D006331:Heart Diseases / Q000503:physiopathology / Q000601:surgery; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007362:Intensive Care Units; D008991:Monitoring, Physiologic / Q000379:methods; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors