PMID- 35549774
OWN - NLM
STAT- MEDLINE
DCOM- 20230321
LR  - 20230321
IS  - 1476-8259 (Electronic)
IS  - 1025-5842 (Linking)
VI  - 26
IP  - 5
DP  - 2023 Apr
TI  - ECG classification system based on multi-domain features approach coupled with 
      least square support vector machine (LS-SVM).
PG  - 540-547
LID - 10.1080/10255842.2022.2072684 [doi]
AB  - Developing a robust authentication and identification method becomes an urgent 
      demand to protect the integrity of devices data. Although the use of passwords 
      provides an acceptable control and authentication, it has shown much weakness in 
      terms of speed and integrity, which make biometrics the ideal authentication 
      solution. As a result, electrocardiogram (ECG) signals have received a great 
      attention in most authentication systems due to the individualized nature of the 
      ECG signals that make them difficult to counterfeit and ubiquitous. In this 
      paper, we propose a new model for ECG verification using multi-domain features 
      coupled with a least square support vector machine (LS-SVM). Two types of 
      features are investigated to find the best set of features to individual from ECG 
      signals. Time domain and frequency domain features based on optimized Triple Band 
      filter bank are extracted from ECG signals. The extracted features are 
      investigated to figure out the best relevant features and remove the redundant 
      ones. The selected features are fed to three classifiers, including Least Square 
      Support Vector Machine (LS-SVM), K-means, and K-nearest. The obtained results 
      have shown that our ECG biometric authentication system outperforms existing 
      methods. The proposed model obtained an average of accuracy of 88%, 95% with time 
      and frequency features, respectively, while it recorded 99% when a combination of 
      time and frequency features are used to classify ECG signals. A public dataset is 
      used to assess the proposed model.
FAU - Majeed, Russel R
AU  - Majeed RR
AD  - College of Education for Pure Sciences, University of Thi-Qar, Nasiriyah, Iraq.
FAU - Alkhafaji, Sarmad K D
AU  - Alkhafaji SKD
AD  - College of Education for Pure Sciences, University of Thi-Qar, Nasiriyah, Iraq.
LA  - eng
PT  - Journal Article
DEP - 20220513
PL  - England
TA  - Comput Methods Biomech Biomed Engin
JT  - Computer methods in biomechanics and biomedical engineering
JID - 9802899
SB  - IM
MH  - *Support Vector Machine
MH  - Least-Squares Analysis
MH  - *Electrocardiography/methods
OTO - NOTNLM
OT  - ECG
OT  - LS-SVM
OT  - authentication
OT  - frequency features
OT  - time domain features
EDAT- 2022/05/14 06:00
MHDA- 2023/03/22 06:00
CRDT- 2022/05/13 12:06
PHST- 2022/05/14 06:00 [pubmed]
PHST- 2023/03/22 06:00 [medline]
PHST- 2022/05/13 12:06 [entrez]
AID - 10.1080/10255842.2022.2072684 [doi]
PST - ppublish
SO  - Comput Methods Biomech Biomed Engin. 2023 Apr;26(5):540-547. doi: 
      10.1080/10255842.2022.2072684. Epub 2022 May 13.