Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques

Hamid Hassanpour,Boualem Boashash,Mostefa Mesbah

doi:10.1155/s1110865704406167

Hamid Hassanpour, Boualem Boashash + Show 1 more

Open Access

https://doi.org/10.1155/s1110865704406167

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Abstract

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.

Highlights

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 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 recognisable
We suggest using a feature selection technique based on the probability distribution function of the SVs (DFSVs)
The results show that the DFSV technique has the overall better performance than the other techniques in terms of the good detection rate (GDR) and false detection rate (FDR)

Summary

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]. 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. Detection of EEG seizures using the low-frequency signature requires a lower number of data samples, the computational time is reduced. Since SVs are orthonormal [10], their squared elements can be treated as probability density function (PDF) [11] These PDFs are used in the process of seizure feature extraction in this paper. By using the SVD technique, the SVs and their importance in the composition of the matrix (singular values) are computed

TFD of signals

TF-based EEG seizure feature extraction

Experimental results and performance comparison

The autocorrelation technique

The spectrum technique

CONCLUSIONS