ECG-Based Subject Identification Using Common Spatial Pattern and SVM
Joint Authors
Alshebeili, Saleh
Alotaiby, Turky N.
Alsabhan, Waleed M.
Aljafar, Latifah M.
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-31
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Alotaiby, Turky N.& Alshebeili, Saleh& Aljafar, Latifah M.& Alsabhan, Waleed M.. 2019. ECG-Based Subject Identification Using Common Spatial Pattern and SVM. Journal of Sensors،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1191780
Modern Language Association (MLA)
Alotaiby, Turky N.…[et al.]. ECG-Based Subject Identification Using Common Spatial Pattern and SVM. Journal of Sensors No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1191780
American Medical Association (AMA)
Alotaiby, Turky N.& Alshebeili, Saleh& Aljafar, Latifah M.& Alsabhan, Waleed M.. ECG-Based Subject Identification Using Common Spatial Pattern and SVM. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1191780
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191780