HRV feature selection for neonatal seizure detection: A wrapper approach

M. B. Malarvili; M. Mesbah; B. Boashash

doi:10.1109/ICSPC.2007.4728456

HRV feature selection for neonatal seizure detection: A wrapper approach

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

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
Title of host publication	ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications
Pages	864-867
Number of pages	4
DOIs	https://doi.org/10.1109/ICSPC.2007.4728456
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
Country	United Arab Emirates
City	Dubai
Period	11/14/07 → 11/27/07

Fingerprint

seizure

Feature extraction

Classifiers

Set theory

Keywords

Feature extraction
Newborn heart rate variability
Seizure
Statistical classifier
Wrapper

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Communication

Cite this

Malarvili, M. B., Mesbah, M., & Boashash, B. (2007). HRV feature selection for neonatal seizure detection: A wrapper approach. In ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications (pp. 864-867). [4728456] https://doi.org/10.1109/ICSPC.2007.4728456

HRV feature selection for neonatal seizure detection : A wrapper approach. / Malarvili, M. B.; Mesbah, M.; Boashash, B.

ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications. 2007. p. 864-867 4728456.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Malarvili, MB, Mesbah, M & Boashash, B 2007, HRV feature selection for neonatal seizure detection: A wrapper approach. in ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications., 4728456, pp. 864-867, 2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007, Dubai, United Arab Emirates, 11/14/07. https://doi.org/10.1109/ICSPC.2007.4728456

Malarvili MB, Mesbah M, Boashash B. HRV feature selection for neonatal seizure detection: A wrapper approach. In ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications. 2007. p. 864-867. 4728456 https://doi.org/10.1109/ICSPC.2007.4728456

Malarvili, M. B. ; Mesbah, M. ; Boashash, B. / HRV feature selection for neonatal seizure detection : A wrapper approach. ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications. 2007. pp. 864-867

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Access to Document

10.1109/ICSPC.2007.4728456