{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:45:00Z","timestamp":1767422700214,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52177193"],"award-info":[{"award-number":["52177193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Shaanxi Province","award":["2022GY-182"],"award-info":[{"award-number":["2022GY-182"]}]},{"name":"China Scholarship Council (CSC) State Scholarship Fund International Clean Energy Talent Project","award":["(Grant No. [2018]5046,[2019]157"],"award-info":[{"award-number":["(Grant No. [2018]5046,[2019]157"]}]},{"name":"Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network, Nanjing Institute of Technology","award":["XTCX202007"],"award-info":[{"award-number":["XTCX202007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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%.<\/jats:p>","DOI":"10.3390\/s22051962","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T22:53:25Z","timestamp":1646261605000},"page":"1962","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The Identification of ECG Signals Using WT-UKF and IPSO-SVM"],"prefix":"10.3390","volume":"22","author":[{"given":"Ning","family":"Li","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-1545","authenticated-orcid":false,"given":"Longhui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2781-1693","authenticated-orcid":false,"given":"Wentao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yelin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuxing","family":"He","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aixiang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Humanities and Foreign Languages, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0995-4989","authenticated-orcid":false,"given":"Xiaoping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic, Electrical, and Systems Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TIFS.2015.2398817","article-title":"Deep Representations for Iris, Face, and Fingerprint Spoofing Detection","volume":"10","author":"Menotti","year":"2015","journal-title":"IEEE Trans. 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