SVD-based newborn EEG seizure detection in the time-frequency domain

Hantid Hassanpour, Mostefa Mesbah, Boualem Boashash

نتاج البحث: المساهمة في مجلةمراجعة النظراء

ملخص

This paper utilises the Singular Value Decomposition (SVD) technique applied to the time-frequency representation of Electroencephalogram (EEG) signals for detecting EEG seizures in neonates. Seizure in EEG signal may have signature in different frequency areas. This paper, is concentrated on the low frequency (lower than 10 Hz) signature of the seizures. The proposed technique uses the estimated distribution function of the singular vectors associated with the time-frequency representation of the EEG epoch to characterise the patterns embedded in the signal. The estimated distributed functions related to the seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns.

اللغة الأصليةEnglish
الصفحات (من إلى)329-333
عدد الصفحات5
دوريةIFAC Proceedings Volumes (IFAC-PapersOnline)
مستوى الصوت36
رقم الإصدار15
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2003
منشور خارجيًانعم
الحدث5th IFAC Symposium on Modelling and Control in Biomedical Systems 2003 - Melbourne
المدة: أغسطس ٢١ ٢٠٠٣أغسطس ٢٣ ٢٠٠٣

ASJC Scopus subject areas

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