TY - GEN
T1 - HRV feature selection for neonatal seizure detection
T2 - 2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007
AU - Malarvili, M. B.
AU - Mesbah, M.
AU - Boashash, B.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Feature extraction
KW - Newborn heart rate variability
KW - Seizure
KW - Statistical classifier
KW - Wrapper
UR - http://www.scopus.com/inward/record.url?scp=60349132394&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=60349132394&partnerID=8YFLogxK
U2 - 10.1109/ICSPC.2007.4728456
DO - 10.1109/ICSPC.2007.4728456
M3 - Conference contribution
AN - SCOPUS:60349132394
SN - 9781424412365
T3 - ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
SP - 864
EP - 867
BT - ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
Y2 - 14 November 2007 through 27 November 2007
ER -