![](/images/graphics-bg.png)
ECG-Based Subject Identification Using Statistical Features and Random Forest
Joint Authors
Alshebeili, Saleh
Alotaiby, Turky N.
Alrshoud, Saud R.
Aljafar, Latifah M.
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-16
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In this work, a nonfiducial electrocardiogram (ECG) identification algorithm based on statistical features and random forest classifier is presented.
Two feature extraction approaches are investigated: direct and band-based approaches.
In the former, eleven simple statistical features are directly extracted from a single-lead ECG signal segment.
In the latter, the single-lead ECG signal is first decomposed into bands, and the statistical features are extracted from each segment of a given band and concatenated to form the feature vector.
Nonoverlapping segments of different lengths (i.e., 1, 3, 5, 7, 10, or 15 sec) are examined.
The extracted feature vectors are applied to a random forest classifier, for the purpose of identification.
This study considers 290 reference subjects from the ECG database of the Physikalisch-Technische Bundesanstalt (PTB).
The proposed identification algorithm achieved an accuracy rate of 99.61% utilizing the single limb lead (I) with the band-based approach.
A single chest lead (V1), augmented limb lead (aVF), and Frank’s lead (Vx) achieved an accuracy rate of 99.37%, 99.76%, and 99.76%, respectively, using the same approach.
American Psychological Association (APA)
Alotaiby, Turky N.& Alrshoud, Saud R.& Alshebeili, Saleh& Aljafar, Latifah M.. 2019. ECG-Based Subject Identification Using Statistical Features and Random Forest. Journal of Sensors،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1191447
Modern Language Association (MLA)
Alotaiby, Turky N.…[et al.]. ECG-Based Subject Identification Using Statistical Features and Random Forest. Journal of Sensors No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1191447
American Medical Association (AMA)
Alotaiby, Turky N.& Alrshoud, Saud R.& Alshebeili, Saleh& Aljafar, Latifah M.. ECG-Based Subject Identification Using Statistical Features and Random Forest. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1191447
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191447