TY - JOUR
T1 - Dominant frequency analysis of EEG reveals brain's response during injury and recovery
AU - Goel, Vaibhava
AU - Brambrink, Ansgar M.
AU - Baykal, Ahmet
AU - Koehler, Raymond C.
AU - Hanley, Daniel F.
AU - Thakor, Nitish V.
N1 - Funding Information:
Manuscript received February 10, 1995; revised June 13, 1996. This work was supported by the National Institute of Health under Grants NS24282 and NS20020. Asterisk indicates corresponding uuthor.
Funding Information:
the B.Tech. degree in electrical engineering from In-dian Institute of Technology, Bombay, in 1974 and the Ph.D. degree in electrical and computer engi-neering from the University of Wisconsin, Madison, in 1981. He served on the faculty of Electrical Engineering and Computer Science of the Northwestern Uni-versity between 1981 and 1983, and since then he has been with the Johns Hopkins University, School of Medicine, Baltimore, MD, where he is currently serving as a Professor of Biomedical Engineering. He teaches and conducts research on cardiovascular and neurological instrumentation, signal processing, and large scale computer applications. Dr. Thakor is an author of more than 80 peer-reviewed publications on these subjects. He serves on the editorial boards of several journals, including the IEEE TRANSACTIOONN SB IOMEDICAELN GINEERINanGd is actively interested in developing international scientific programs, collaborative exchanges, tutorials, and books on the topics of Biomedical Signal Processing, Neuro-engineering, and High Performance Computers in Biomedical Engineering. He is a recipient of a Research Career Development Award from the National Institutes of Health and a Presidential Young Investigator Award from the National Science Foundation, and a fellow of the American Institute of Medical and Biological Engineering.
PY - 1996
Y1 - 1996
N2 - 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 six was obtained for these data, resulting in three 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 our data is the spectral dispersion, or diverging trends in the three 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 three 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 three 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.
AB - 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 six was obtained for these data, resulting in three 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 our data is the spectral dispersion, or diverging trends in the three 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 three 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 three 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.
UR - http://www.scopus.com/inward/record.url?scp=0029914191&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029914191&partnerID=8YFLogxK
U2 - 10.1109/10.541250
DO - 10.1109/10.541250
M3 - Article
C2 - 9214826
AN - SCOPUS:0029914191
SN - 0018-9294
VL - 43
SP - 1083
EP - 1092
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
IS - 11
ER -