DocumentCode :
1825571
Title :
Multichannel-Based Newborn EEG Seizure Detection using Time-Frequency Matched Filter
Author :
Khlif, M.S. ; Mesbah, M. ; Boashash, B. ; Colditz, Paul
Author_Institution :
Univ. of Queensland, Brisbane
fYear :
2007
fDate :
22-26 Aug. 2007
Firstpage :
1265
Lastpage :
1268
Abstract :
In recent years, much effort has been made toward developing computerized methods to detect seizures. In adults, the clinical signs of seizures are well defined and easily recognizable. But in newborns, these signs are either subtle or completely absent. For this reason, the electroencephalogram (EEG) has been the most dependable tool used for detecting seizures in newborns. Considering the non-stationary and multicomponent nature of the EEG signals, time- frequency (TF) based methods were found to be very suitable for the analysis of such signals. Using TF representation of EEG signals allows extracting TF signatures that are characteristic of EEG seizures. In this paper we present a TF method for newborn EEG seizure detection using a TF matched filter. The threshold used to distinguish between seizure and non- seizure is data-dependent and is set using the EEG background. Multichannel geometrical correlation, based on a concept of incidence matrix, was utilized to further enhance the performance of the detector.
Keywords :
band-pass filters; data acquisition; electroencephalography; medical signal processing; neurophysiology; paediatrics; time-frequency analysis; bandpass filter; data acquisition; data labeling; electroencephalogram; incidence matrix concept; linear frequency modulated pattern; multichannel geometrical correlation; multichannel-based newborn EEG seizure detection; time-frequency matched filter; Detectors; Electrodes; Electroencephalography; Matched filters; Pediatrics; Scalp; Signal analysis; Signal processing; Time frequency analysis; Voltage; Algorithms; Artificial Intelligence; Brain Mapping; Diagnosis, Computer-Assisted; Electroencephalography; Humans; Infant, Newborn; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
ISSN :
1557-170X
Print_ISBN :
978-1-4244-0787-3
Type :
conf
DOI :
10.1109/IEMBS.2007.4352527
Filename :
4352527
Link To Document :
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