Abstract
During recovery following episodes of hypoxic-ischemic (HI) injury in the neonate, the electroencephalogram (EEG) recovers with sporadic, fluctuating energy discharges known as `bursts' and periods of electrical silence (`burst suppression'). Prior to the resumption of normal activity, the individual pattern of bursting may hold important diagnostic information regarding neurological outcome. Detection and characterization of the bursts seems to be an important factor in understanding the dynamics of the recovering EEG. The Teager Energy Algorithm (TEA) is a new method to describe abrupt EEG energy changes. Prior to the resumption of continuous EEG we coupled the use of the TEA and sequential detection to describe the start and stop time of bursts. Employing a dominant frequency model, the TEA provides distortion free reproduction of signal energy without the need for filtering high or sum frequency components portions. In an animal model of neonatal HI injury, we showed that TEA provides efficient detection of burst and burst suppression episodes. Burst counts might provide indicators of neurological and behavioral outcomes.
| Original language | English (US) |
|---|---|
| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Publisher | IEEE |
| Pages | 1188-1190 |
| Number of pages | 3 |
| Volume | 3 |
| State | Published - 1997 |
| Externally published | Yes |
| Event | Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA Duration: Oct 30 1997 → Nov 2 1997 |
Other
| Other | Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
|---|---|
| City | Chicago, IL, USA |
| Period | 10/30/97 → 11/2/97 |
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ASJC Scopus subject areas
- Bioengineering
Cite this
Detecting EEG bursts after hypoxic-ischemic injury using energy operators. / Sherman, David L.; Brambrink, Ansgar; Walterspacher, Dirk; Dasika, Vasant K.; Ichord, Rebecca; Thakor, Nitish V.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Vol. 3 IEEE, 1997. p. 1188-1190.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Detecting EEG bursts after hypoxic-ischemic injury using energy operators
AU - Sherman, David L.
AU - Brambrink, Ansgar
AU - Walterspacher, Dirk
AU - Dasika, Vasant K.
AU - Ichord, Rebecca
AU - Thakor, Nitish V.
PY - 1997
Y1 - 1997
N2 - During recovery following episodes of hypoxic-ischemic (HI) injury in the neonate, the electroencephalogram (EEG) recovers with sporadic, fluctuating energy discharges known as `bursts' and periods of electrical silence (`burst suppression'). Prior to the resumption of normal activity, the individual pattern of bursting may hold important diagnostic information regarding neurological outcome. Detection and characterization of the bursts seems to be an important factor in understanding the dynamics of the recovering EEG. The Teager Energy Algorithm (TEA) is a new method to describe abrupt EEG energy changes. Prior to the resumption of continuous EEG we coupled the use of the TEA and sequential detection to describe the start and stop time of bursts. Employing a dominant frequency model, the TEA provides distortion free reproduction of signal energy without the need for filtering high or sum frequency components portions. In an animal model of neonatal HI injury, we showed that TEA provides efficient detection of burst and burst suppression episodes. Burst counts might provide indicators of neurological and behavioral outcomes.
AB - During recovery following episodes of hypoxic-ischemic (HI) injury in the neonate, the electroencephalogram (EEG) recovers with sporadic, fluctuating energy discharges known as `bursts' and periods of electrical silence (`burst suppression'). Prior to the resumption of normal activity, the individual pattern of bursting may hold important diagnostic information regarding neurological outcome. Detection and characterization of the bursts seems to be an important factor in understanding the dynamics of the recovering EEG. The Teager Energy Algorithm (TEA) is a new method to describe abrupt EEG energy changes. Prior to the resumption of continuous EEG we coupled the use of the TEA and sequential detection to describe the start and stop time of bursts. Employing a dominant frequency model, the TEA provides distortion free reproduction of signal energy without the need for filtering high or sum frequency components portions. In an animal model of neonatal HI injury, we showed that TEA provides efficient detection of burst and burst suppression episodes. Burst counts might provide indicators of neurological and behavioral outcomes.
UR - http://www.scopus.com/inward/record.url?scp=0031294923&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031294923&partnerID=8YFLogxK
M3 - Chapter
VL - 3
SP - 1188
EP - 1190
BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PB - IEEE
ER -