Abstract
This paper presents an improved time-frequency (TF) based technique for newborn EEG seizure detection. The original technique analyses successive spikes intervals of the EEG signal in the TF domain to discriminate between seizure and nonseizure activities. In this paper improvement on the original approach is achieved by using a new spike detection technique. In this technique the TF of the signal is enhanced before the actual spike detection scheme is applied. Then, two frequency slices are extracted from the higher frequency area of the TF distribution to detect the spikes. The extracted frequency slices are subjected to the smoothed nonlinear energy operator to accentuate the spike signatures. Histogram of successive spikes intervals is then used as a feature for seizure detection. In the presented technique the EEG data are segmented into 4-second epochs. A k-nearest neighbour algorithm is employed to classify the EEG epochs into seizure and nonseizure groups. The performance of the presented technique is evaluated using the EEG data of five neonates.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 |
| Publisher | IEEE Computer Society |
| Pages | 41-44 |
| Number of pages | 4 |
| Volume | 2 |
| ISBN (Print) | 0780379462, 9780780379466 |
| DOIs | |
| Publication status | Published - 2003 |
| Event | 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 - Paris, France Duration: Jul 1 2003 → Jul 4 2003 |
Other
| Other | 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 |
|---|---|
| Country | France |
| City | Paris |
| Period | 7/1/03 → 7/4/03 |
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ASJC Scopus subject areas
- Signal Processing
Cite this
Neonatal EEG seizure detection using spike signatures in the time-frequency domain. / Hassanpour, Hamid; Mesbah, Mostefa.
Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003. Vol. 2 IEEE Computer Society, 2003. p. 41-44 1224810.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neonatal EEG seizure detection using spike signatures in the time-frequency domain
AU - Hassanpour, Hamid
AU - Mesbah, Mostefa
PY - 2003
Y1 - 2003
N2 - This paper presents an improved time-frequency (TF) based technique for newborn EEG seizure detection. The original technique analyses successive spikes intervals of the EEG signal in the TF domain to discriminate between seizure and nonseizure activities. In this paper improvement on the original approach is achieved by using a new spike detection technique. In this technique the TF of the signal is enhanced before the actual spike detection scheme is applied. Then, two frequency slices are extracted from the higher frequency area of the TF distribution to detect the spikes. The extracted frequency slices are subjected to the smoothed nonlinear energy operator to accentuate the spike signatures. Histogram of successive spikes intervals is then used as a feature for seizure detection. In the presented technique the EEG data are segmented into 4-second epochs. A k-nearest neighbour algorithm is employed to classify the EEG epochs into seizure and nonseizure groups. The performance of the presented technique is evaluated using the EEG data of five neonates.
AB - This paper presents an improved time-frequency (TF) based technique for newborn EEG seizure detection. The original technique analyses successive spikes intervals of the EEG signal in the TF domain to discriminate between seizure and nonseizure activities. In this paper improvement on the original approach is achieved by using a new spike detection technique. In this technique the TF of the signal is enhanced before the actual spike detection scheme is applied. Then, two frequency slices are extracted from the higher frequency area of the TF distribution to detect the spikes. The extracted frequency slices are subjected to the smoothed nonlinear energy operator to accentuate the spike signatures. Histogram of successive spikes intervals is then used as a feature for seizure detection. In the presented technique the EEG data are segmented into 4-second epochs. A k-nearest neighbour algorithm is employed to classify the EEG epochs into seizure and nonseizure groups. The performance of the presented technique is evaluated using the EEG data of five neonates.
UR - http://www.scopus.com/inward/record.url?scp=33847246556&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847246556&partnerID=8YFLogxK
U2 - 10.1109/ISSPA.2003.1224810
DO - 10.1109/ISSPA.2003.1224810
M3 - Conference contribution
SN - 0780379462
SN - 9780780379466
VL - 2
SP - 41
EP - 44
BT - Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
PB - IEEE Computer Society
ER -