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

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Title: Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques
Author: Hassanpour, Hamid; Mesbah, Mostefa; Boashash, Boualem
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.
Description: This paper reviews the combined use of SVD and TFSA for newborn seizure detection. (The most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia., and then continuously updated).
URI: http://hdl.handle.net/10576/10707
Date: 2004-01

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