Paper Info
Title
An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers
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 non-contact 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: k-nearest 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, cross-correlation 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 (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .
Year
DOI
Venue
2016
10.1007/s11517-015-1393-5
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Arterial pulse waveform,Feature creation,K-nearest neighbour algorithm,Optical system,Recursive feature elimination,Support vector machine
Time domain,Frequency domain,Computer vision,Pattern recognition,Feature selection,Feature (computer vision),Waveform,Support vector machine,Artificial intelligence,Amplitude,Mathematics,Wavelet
Journal
Volume
Issue
ISSN
54
7
1741-0444
Citations 
PageRank 
References 
5
0.48
16
Authors
4
Name
Order
Citations
PageRank
Tânia Pereira1248.61
joana s paiva250.48
A. Correia3198.62
João M. Cardoso420811.81