Authors: Tânia Pereira Joana S Paiva Carlos Correia João Cardoso
Publish Date: 2015/09/24
Volume: 54, Issue: 7, Pages: 1049-1059
Abstract
The measurement and analysis of the arterial pulse waveform APW are the means for cardiovascular risk assessment Optical sensors represent an attractive instrumental solution to APW assessment due to their truly noncontact nature that makes the measurement of the skin surface displacement possible especially at the carotid artery site In this work an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed Two classifiers were implemented knearest neighbours and support vector machine SVM and a comparative study was made considering widely used performance metrics This work represents a wide study in feature creation for APW A pool of 37 features was extracted and split in different subsets amplitude features time domain statistics wavelet features crosscorrelation features and frequency domain statistics The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature The best result 0952 accuracy in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset
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