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

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.

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Correspondence to Hamid Hassanpour.

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Hassanpour, H., Mesbah, M. & Boashash, B. Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques. EURASIP J. Adv. Signal Process. 2004, 898124 (2004). https://doi.org/10.1155/S1110865704406167

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Keywords and phrases

  • detection
  • time-frequency distribution
  • singular value decomposition
  • probability distribution function
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