Abstract
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.
Original language | English |
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Title of host publication | ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications |
Pages | 864-867 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2007 |
Event | 2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007 - Dubai, United Arab Emirates Duration: Nov 14 2007 → Nov 27 2007 |
Other
Other | 2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007 |
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Country | United Arab Emirates |
City | Dubai |
Period | 11/14/07 → 11/27/07 |
Keywords
- Feature extraction
- Newborn heart rate variability
- Seizure
- Statistical classifier
- Wrapper
ASJC Scopus subject areas
- Computer Networks and Communications
- Signal Processing
- Communication