Neonatal EEG seizure detection using spike signatures in the time-frequency domain

Hassanpour, H. and Mesbah, M. (2003). Neonatal EEG seizure detection using spike signatures in the time-frequency domain. In: Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, 2003.. Seventh International Symposium on Signal Processing and Its Applications, 2003., Paris, France, (41-44). 1-4 July, 2003. doi:10.1109/ISSPA.2003.1224810


Author Hassanpour, H.
Mesbah, M.
Title of paper Neonatal EEG seizure detection using spike signatures in the time-frequency domain
Conference name Seventh International Symposium on Signal Processing and Its Applications, 2003.
Conference location Paris, France
Conference dates 1-4 July, 2003
Proceedings title Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, 2003.
Journal name Seventh International Symposium On Signal Processing and its Applications, Vol 2, Proceedings
Place of Publication Piscataway
Publisher IEEE
Publication Year 2003
Sub-type Fully published paper
DOI 10.1109/ISSPA.2003.1224810
Open Access Status
ISBN 0-7803-7946-2
Volume 2
Start page 41
End page 44
Total pages 4
Language eng
Abstract/Summary 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.
Subjects 090609 Signal Processing
0903 Biomedical Engineering
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Excellence in Research Australia (ERA) - Collection
School of Public Health Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 9 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 26 Mar 2009, 00:55:32 EST by Maryanne Watson on behalf of Library Corporate Services