TY - GEN
T1 - HRV feature selection based on discriminant and redundancy analysis for neonatal seizure detection
AU - Malarvili, M. B.
AU - Mesbah, M.
AU - Boashash, B.
PY - 2007
Y1 - 2007
N2 - This paper addresses the feature selection problem by using a discriminant and redundancy based method to select a feature subset with high discriminatory power between the classes of newborn heart rate variability (HRV) corresponding to seizure and non-seizure. The proposed method combines the Fast Correlation Based Filter (FCBF) criteria for redundancy analysis with the area under the Receiver Operating Curves (AUC) for discriminant analysis. The classification accuracies of the selected features were compared using 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 a significant reduction in feature dimensionality while achieving 85.7% sensitivity and 84.6% specificity.
AB - This paper addresses the feature selection problem by using a discriminant and redundancy based method to select a feature subset with high discriminatory power between the classes of newborn heart rate variability (HRV) corresponding to seizure and non-seizure. The proposed method combines the Fast Correlation Based Filter (FCBF) criteria for redundancy analysis with the area under the Receiver Operating Curves (AUC) for discriminant analysis. The classification accuracies of the selected features were compared using 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 a significant reduction in feature dimensionality while achieving 85.7% sensitivity and 84.6% specificity.
KW - Feature extraction
KW - Feature selection-filter
KW - Heart rate variability
KW - Newborn seizure detection
KW - Statistical classifier
UR - http://www.scopus.com/inward/record.url?scp=50449091090&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50449091090&partnerID=8YFLogxK
U2 - 10.1109/ICICS.2007.4449765
DO - 10.1109/ICICS.2007.4449765
M3 - Conference contribution
AN - SCOPUS:50449091090
SN - 1424409837
SN - 9781424409839
T3 - 2007 6th International Conference on Information, Communications and Signal Processing, ICICS
BT - 2007 6th International Conference on Information, Communications and Signal Processing, ICICS
T2 - 2007 6th International Conference on Information, Communications and Signal Processing, ICICS
Y2 - 10 December 2007 through 13 December 2007
ER -