DC-level Detection of Burst-Suppression EEG

T. Lipping; P. Loula; V. Jäntti; A Yli-Hankala

doi:10.1055/s-0038-1634968

Methods of Information in Medicine, Table of Contents

Methods Inf Med 1994; 33(01): 35-38
DOI: 10.1055/s-0038-1634968

Original Article

Schattauer GmbH

DC-level Detection of Burst-Suppression EEG

Authors

T. Lipping

¹Signal Processing Laboratory Tampere Technical University, Finland
P. Loula

¹Signal Processing Laboratory Tampere Technical University, Finland
V. Jäntti

¹Signal Processing Laboratory Tampere Technical University, Finland
A Yli-Hankala

¹Signal Processing Laboratory Tampere Technical University, Finland

Abstract

Abstract:

The EEG signal is usually recorded with low time constant analog prefilters to avoid low frequency artefacts. During this kind of recording the frequency components below the cutoff frequency of the analog prefilter (usually below about 1 to 3 Hz) are lost. By visual examination of some experimental recordings taken with a higher time constant, it was noticed that during burst-suppression EEG the DC-level of the signal rises sharply when the burst begins and falls when the burst ends. Thus, a burst actually consists of a mixed frequency discharge on a pulse-like DC-shift. We developed a filter algorithm to estimate the change in the DC-level during bursts as accurately as possible.

Keywords

Detection - EEG - FMH Filtering - Burst-Suppression - Anesthesia

Full Text

References

REFERENCES
1 Swank RL, Watson CW. Effects of barbiturates and ether on spontaneous electrical activity of dog brain. J Neurophysiol 1949; 12: 137-60.
2 Jäntti V, Yli-Hankala A. Correlation of instantaneous heart rate and EEG suppression during enflurane anaesthesia: Synchronous inhibition of heart rate and cortical electrical activity. Electroenceph Clin Neurophysiol 1990; 76: 476-9.
3 Jäntti V, Yli-Hankala A, Baer GA, Porkkala T. Slow potentials of EEG burst suppression pattern during anaesthesia. Acta Anaesthe-siol Scand 1993; 37: 121-3.
4 Heinonen P, Neuvo Y. FIR-median hybrid filters with predictive substructures. IEEE Trans Acoust Speech Signal Processing 1988; 36: 892-9.
5 Nieminen A, Neuvo Y, Värri A, Mitra U. Algorithms for real-time trend detection. Signal Processing 1989; 18: 1-15.
6 Wichman R, Heinonen P, Neuvo Y. FIR-median hybrid filters with excellent transient response in noisy conditions. In: Int Conf Digital Signal Processing, Florence, Italy 1987; 171-5.