Quantification of EEG variability

[Long-term EEG monitoring in patients with acute traumatic brain injury]

OBJECTIVE

To investigate the informativeness of long-term scalp EEG monitoring in patients with acute traumatic brain injury (TBI).

MATERIAL AND METHODS

The informativity of long-term EEG monitoring (LTM) was performed in 60 patients with acute severe TBI. Odd ratios (OR) of unfavorable outcome and non-convulsive status epilepticus (NCSE) among clinical, neurophysiological and radiological features were calculated.

RESULTS

EEG features of the unfavorable outcome are: slowing of the dominant background rhythm below q range (OR 3.5, CI 1.2-10.7), absence of frontal-occipital gradient (OR 10.2, CI 1.89-10.12), absence of reactivity (OR 8.75, CI 2.14-35.7), absence of variability (OR 6.25, CI 1.72-22.6) and absence of NREM sleep, stage 2 (OR 5.8, CI 1.79-18.91). Clinical features associated with the unfavorable outcome are: a decrease in GCS score (OR 1.25, CI 1.07-1.47), TBI severity (OR 2.46, CI 1.16-5.18), axial dislocation (OR 4.45, CI 1.08-18.29). ORs for NCSE are significant for the following EEG features: presence of rhythmic and periodic patterns (RPP) (OR 11.92, CI 1.37-103.39), stimulus induced RPP (OR 23.14, CI 2.56-209.34), "plus" modifier (OR 4.11, CI 1.13-14.91) and electrographic evolution (OR 13.05, CI 3.59-47.39). Background rhythm slowing below q range reduces NCSE probability (OR 3.33, CI 1.09-10).

CONCLUSION

Long-term EEG monitoring is an informative tool for prognosis of outcome and diagnosis of NCSE in patients with severe TBI. The risk of NCSE increases with Marshall score but NCSE is not associated with poor outcome that requires an individual selection of intensive care.

Piece-wise EEG analysis: an objective evaluation

In this paper, a method is described to evaluate EEGs by means of a piece-wise analysis. The procedure involves the recursive computation of a 5th-order autoregressive model by means of a Kalman filter. As an illustration the result of applying this method to sleep recordings is described in this paper. Also, an objective comparison of this method with a more conventional approach (based on analyzing 30-s intervals), and with adaptive segmentation (Praetorius et al., 1977) was carried out using the same data. The results obtained indicate that our method is useful in extracting elementary patterns from an EEG and that the piece-wise analysis approach is to be favored over more conventional techniques.

Demonstration of segmentation techniques for EEG records

In this paper three different techniques for segmenting EEG's are presented. The principles of these techniques (the Kalman filter approach, the power spectrum analysis and the texture matrix approach) are explained and the results obtained summarised. The segmentation is used in an interactive EEG interpretation system.