Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques
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Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques Hamid Hassanpour Lab of Signal Processing Research, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia Email: [email protected]
Mostefa Mesbah Lab of Signal Processing Research, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia Email: [email protected]
Boualem Boashash Lab of Signal Processing Research, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia Email: [email protected] Received 27 August 2003; Revised 17 May 2004 The nonstationary and multicomponent nature of newborn EEG seizures tends to increase the complexity of the seizure detection problem. In dealing with this type of problems, time-frequency-based techniques were shown to outperform classical techniques. This paper presents a new time-frequency-based EEG seizure detection technique. The technique uses an estimate of the distribution function of the singular vectors associated with the time-frequency distribution of an EEG epoch to characterise the patterns embedded in the signal. The estimated distribution functions related to seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns. Keywords and phrases: detection, time-frequency distribution, singular value decomposition, probability distribution function.
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INTRODUCTION
Clinical signs of central nervous system dysfunctions in the neonate are often revealed by seizures which are the results of synchronous discharge of a large number of neurons [1]. Seizures increase the risk of impaired neurological and developmental functioning in neonatal period and also increase the risk of death [2]. Clinical manifestations of seizure in adults such as body jerking, repetitive winking, or fluttering of eyelids are well defined and easily recognisable. However, in newborns, the clinical signs are not as clear and can be missed without constant and close supervision. In neonates, the brain function is constantly changing as its neurophysiology matures [3, 4]. This emphasises the nonstationary behaviour of the electroencephalogram (EEG) in neonates [5, 6]. In addition, the frequency spectrum of the background EEG largely overlaps with the seizure one [7]. This behaviour makes the task of analysing newborn EEG signal a complex one for both neurologists and signal analysts. To overcome this complexity, time-frequency- (TF) based techniques were introduced.
Neonatal EEG seizures have signatures in both low frequency (as low as 0.5 Hz) [8] and high frequency (higher than 70 Hz) [9]. This study concentrates on using the lowfrequency signatures for seizure detection. Detection of EEG seizures using the low-frequency signature requires a lower number of data samples, hence the computational time is reduced. To remove the high-frequency activity, the signal is filtered by a lowpass filter with a cutoff frequency 10 Hz. The filtered signal is then segmented into 30-secon
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