HRV feature selection for neonatal seizure detection: A wrapper approach

M. B. Malarvili, M. Mesbah, B. Boashash

نتاج البحث

7 اقتباسات (Scopus)

ملخص

This work addresses the feature selection problem using a wrapper approach to select a feature subset to distinguish between the classes of newborn heart rate variability (HRV) corresponding to seizure and non-seizure. The method utilizes a filter as a pre-step to remove the irrelevant and redundant features from the original set of features to provide a starting feature subset for the wrapper. This reduces the computation load and the severity of the search operations involved in a wrapper approach. The goodness of the feature subset selected is compared over 3 different classifiers, namely linear classifier, quadratic classifier and k-Nearest Neighbour (k-NN) statistical classifiers in a leave-one-out (LOO) cross validation. It was found that the 1-NN outperformed the other classifiers resulting in significant reductions in feature dimensionality and achieving 85.7% sensitivity and 84.6% specificity.

اللغة الأصليةEnglish
عنوان منشور المضيفICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
الصفحات864-867
عدد الصفحات4
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2007
منشور خارجيًانعم
الحدث2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007 - Dubai
المدة: نوفمبر ١٤ ٢٠٠٧نوفمبر ٢٧ ٢٠٠٧

سلسلة المنشورات

الاسمICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications

Other

Other2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007
الدولة/الإقليمUnited Arab Emirates
المدينةDubai
المدة١١/١٤/٠٧١١/٢٧/٠٧

ASJC Scopus subject areas

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