Dominant frequency analysis of EEG reveals brain's response during injury and recovery
- 1 January 1996
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 43 (11) , 1083-1092
- https://doi.org/10.1109/10.541250
Abstract
A new method of monitoring and analyzing electroencephalogram (EEG) signals during brain injury is presented, EEG signals are modeled using the autoregressive (AR) technique to obtain the frequencies where there are peaks in the spectrum. The powers at these dominant frequencies are analyzed to reveal the state of brain injury during an experimental study involving progressive hypoxia, asphyxia, and recovery. Neonatal piglets (n=8) were exposed to a sequence of 30 min of hypoxia (10% oxygen), 5 min of room air, and 7 min of asphyxia. They then received cardiopulmonary resuscitation and were subsequently monitored for 4 h. An optimal AR model order of 6 was obtained for these data, resulting in 3 dominant frequencies. These dominant frequencies, referred to as the low, medium, and high frequency components, fell in the bands 1.0-5.5 Hz, 9.0-14.0 Hz, and 18.0-21.0 Hz, respectively. A remarkable feature of the authors' data is the spectral dispersion, or diverging trends in the 3 frequency bands. During hypoxia, the relative powers of the medium and high-frequency components of EEG increased up to 160% and 176%, from their respective baseline values. During the first minute of asphyxia the medium- and high-frequency powers (relative to baseline) increased by 280-400%. The power in all 3 frequency components went down to nearly zero within 40-80 s of asphyxia. During recovery, the phenomenon of burst-suppression was clearly exhibited in the low-frequency component. A new index, called mean normalized separation, representing the degree of disproportionality in the recovery of powers of the 3 dominant components relative to their mean recovered power, is presented as a possible single indicator of electrical function recovery. In conclusion, dominant frequency analysis helps reveal the brain's graded electrical response to injury and recovery.Keywords
This publication has 25 references indexed in Scilit:
- Well-developed infant with hypoxic-ischemic encephalopathy associated with EEG burst suppression and subcortical leukohypodensity on CT scanPediatrics International, 1993
- Analysis of rat EEG using autoregressive power spectraJournal of Neuroscience Methods, 1991
- Identification of EEG Patterns Occuring in Anesthesia by Means of Autoregressive Parameters. Erkennung von Narkose-EEG-Mustern mit Hilfe autoregressiver ParameterBiomedizinische Technik/Biomedical Engineering, 1991
- POWER SPECTRAL ANALYSIS OF THE EEG OF TERM INFANTS FOLLOWING BIRTH ASPHYXIADevelopmental Medicine and Child Neurology, 1990
- A new method of presentation of the average spectral properties of the EEG time seriesInternational Journal of Bio-Medical Computing, 1988
- Classification of the EEG during neurosurgery. Parametric identification and Kalman filtering comparedJournal of Biomedical Engineering, 1986
- Automated EEG Processing for Intraoperative MonitoringAnesthesiology, 1980
- General anaesthesia and changes on the cerebral function monitorAnaesthesia, 1978
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974
- Correlation of Continuous Electroencephalograms With Cerebral Blood Flow Measurements During Carotid EndarterectomyStroke, 1973