Discrete wavelet transform based seizure detection in newborns EEG signals

Pega Zarjam, Mostefa Mesbah

نتاج البحث

5 اقتباسات (Scopus)

ملخص

This paper proposes a novel method for detecting newborns seizure events from electroencephalogram (EEG) data. The detection scheme is based on the discrete wavelet transform (DWT) of the EEG signals. The number of zero-crossings, the average distance between adjacent zero-crossings, the number of extrema, and the average distance between adjacent extrema of the wavelet coefficients (WCs) of certain scales are extracted to form a feature set. The extracted feature set is then fed to an artificial neural network (ANN) classifier to organize the EEG signals into seizure and non- seizure activities. In this study, the training and test sets were obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The obtained results show that on the average 95% of the EEG seizures were detected by the proposed scheme.

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
ناشرIEEE Computer Society
الصفحات459-462
عدد الصفحات4
رقم المعيار الدولي للكتب (المطبوع)0780379462, 9780780379466
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2003
منشور خارجيًانعم
الحدث7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 - Paris
المدة: يوليو ١ ٢٠٠٣يوليو ٤ ٢٠٠٣

سلسلة المنشورات

الاسمProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
مستوى الصوت2

Other

Other7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
الدولة/الإقليمFrance
المدينةParis
المدة٧/١/٠٣٧/٤/٠٣

ASJC Scopus subject areas

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