PMID- 35271105
OWN - NLM
STAT- MEDLINE
DCOM- 20220314
LR  - 20220317
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 5
DP  - 2022 Mar 2
TI  - The Identification of ECG Signals Using WT-UKF and IPSO-SVM.
LID - 10.3390/s22051962 [doi]
LID - 1962
AB  - The biometric identification method is a current research hotspot in the pattern 
      recognition field. Due to the advantages of electrocardiogram (ECG) signals, 
      which are difficult to replicate and easy to obtain, ECG-based identity 
      identification has become a new direction in biometric recognition research. In 
      order to improve the accuracy of ECG signal identification, this paper proposes 
      an ECG identification method based on a multi-scale wavelet transform combined 
      with the unscented Kalman filter (WT-UKF) algorithm and the improved particle 
      swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm 
      can effectively eliminate the noise components and preserve the characteristics 
      of ECG signals when denoising the ECG data. Then, the wavelet positioning method 
      is used to detect the feature points of the denoised signals, and the obtained 
      feature points are combined with multiple feature vectors to characterize the ECG 
      signals, thus reducing the data dimension in identity identification. Finally, 
      SVM is used for ECG signal identification, and the improved particle swarm 
      optimization (IPSO) algorithm is used for parameter optimization in SVM. 
      According to the analysis of simulation experiments, compared with the 
      traditional WT denoising, the WT-UKF method proposed in this paper improves the 
      accuracy of feature point detection and increases the final recognition rate by 
      1.5%. The highest recognition accuracy of a single individual in the entire ECG 
      identification system achieves 100%, and the average recognition accuracy can 
      reach 95.17%.
FAU - Li, Ning
AU  - Li N
AD  - School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, 
      China.
FAU - Zhu, Longhui
AU  - Zhu L
AUID- ORCID: 0000-0002-9738-1545
AD  - School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, 
      China.
FAU - Ma, Wentao
AU  - Ma W
AUID- ORCID: 0000-0002-2781-1693
AD  - School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, 
      China.
FAU - Wang, Yelin
AU  - Wang Y
AD  - School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, 
      China.
FAU - He, Fuxing
AU  - He F
AD  - School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, 
      China.
FAU - Zheng, Aixiang
AU  - Zheng A
AD  - School of Humanities and Foreign Languages, Xi'an University of Technology, Xi'an 
      710048, China.
FAU - Zhang, Xiaoping
AU  - Zhang X
AUID- ORCID: 0000-0003-0995-4989
AD  - Department of Electronic, Electrical, and Systems Engineering, School of 
      Engineering, University of Birmingham, Birmingham B15 2TT, UK.
LA  - eng
GR  - 52177193/National Natural Science Foundation of China/
GR  - 2022GY-182/Key Research and Development Program of Shaanxi Province/
GR  - (Grant No. [2018]5046,[2019]157/China Scholarship Council (CSC) State Scholarship 
      Fund International Clean Energy Talent Project/
GR  - XTCX202007/Open Research Fund of Jiangsu Collaborative Innovation Center for 
      Smart Distribution Network, Nanjing Institute of Technology/
PT  - Journal Article
DEP - 20220302
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - Electrocardiography/methods
MH  - *Signal Processing, Computer-Assisted
MH  - *Support Vector Machine
MH  - Wavelet Analysis
PMC - PMC8915117
OTO - NOTNLM
OT  - electrocardiogram identification
OT  - improved particle swarm optimization
OT  - parameter optimization
OT  - support vector machine
OT  - unscented Kalman filter
OT  - wavelet transform
COIS- The authors declare no conflict of interest.
EDAT- 2022/03/11 06:00
MHDA- 2022/03/15 06:00
PMCR- 2022/03/02
CRDT- 2022/03/10 15:42
PHST- 2022/01/10 00:00 [received]
PHST- 2022/02/17 00:00 [revised]
PHST- 2022/02/28 00:00 [accepted]
PHST- 2022/03/10 15:42 [entrez]
PHST- 2022/03/11 06:00 [pubmed]
PHST- 2022/03/15 06:00 [medline]
PHST- 2022/03/02 00:00 [pmc-release]
AID - s22051962 [pii]
AID - sensors-22-01962 [pii]
AID - 10.3390/s22051962 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Mar 2;22(5):1962. doi: 10.3390/s22051962.