ECG-Based Subject Identification Using Common Spatial Pattern and SVM

In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and mul...

Full description

Saved in:
Bibliographic Details
Published in:Journal of sensors 2019-01, Vol.2019 (2019), p.1-9
Main Authors: Alsabhan, Waleed M., Aljafar, Latifah M., Alshebeili, Saleh, Alotaiby, Turky N.
Format: Article
Language:English
Subjects:
Access control
Algorithms
Basis functions
Biometric identification
Biometrics
Brain research
Convulsions & seizures
Echocardiography
Electrocardiography
Electroencephalography
Engineering
Feature extraction
International conferences
Pattern recognition
Radial basis function
Recording
Researchers
Signal processing
Spatial analysis
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.
ISSN:1687-725X
1687-7268
DOI:10.1155/2019/8934905