{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T12:25:58Z","timestamp":1768479958882,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"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"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022GY-182"],"award-info":[{"award-number":["2022GY-182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["[2018]5046,[2019]157"],"award-info":[{"award-number":["[2018]5046,[2019]157"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Shaanxi Province","award":["52177193"],"award-info":[{"award-number":["52177193"]}]},{"name":"Key Research and Development Program of Shaanxi Province","award":["2022GY-182"],"award-info":[{"award-number":["2022GY-182"]}]},{"name":"Key Research and Development Program of Shaanxi Province","award":["[2018]5046,[2019]157"],"award-info":[{"award-number":["[2018]5046,[2019]157"]}]},{"name":"China Scholarship Council (CSC) State Scholarship Fund International Clean Energy Talent Project","award":["52177193"],"award-info":[{"award-number":["52177193"]}]},{"name":"China Scholarship Council (CSC) State Scholarship Fund International Clean Energy Talent Project","award":["2022GY-182"],"award-info":[{"award-number":["2022GY-182"]}]},{"name":"China Scholarship Council (CSC) State Scholarship Fund International Clean Energy Talent Project","award":["[2018]5046,[2019]157"],"award-info":[{"award-number":["[2018]5046,[2019]157"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.<\/jats:p>","DOI":"10.3390\/s22124343","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T02:01:44Z","timestamp":1655085704000},"page":"4343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Identification of ECG Signals Using Wavelet Transform and WOA-PNN"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9014-5913","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China"}]},{"given":"Fuxing","family":"He","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"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"}]},{"given":"Ruotong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}]},{"given":"Lin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, D., Si, Y., Yang, W., Zhang, G., and Liu, T. (2019). A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification. Appl. Sci., 9.","DOI":"10.3390\/app9010201"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1109\/TBME.2016.2607020","article-title":"Highly Reliable Key Generation From Electrocardiogram (ECG)","volume":"64","author":"Karimian","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/TIFS.2018.2804890","article-title":"Liveness Detection and Automatic Template Updating Using Fusion of ECG and Fingerprint","volume":"13","author":"Komeili","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/TIFS.2018.2876838","article-title":"Cancelable Biometric Recognition with ECGs: Subspace-Based Approaches","volume":"14","author":"Wu","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Barros, A., Resque, P., Almeida, J., Mota, R., Oliveira, H., Rosario, D., and Cerqueira, E. (2020). Data Improvement Model Based on ECG Biometric for User Authentication and Identification. Sensors, 20.","DOI":"10.3390\/s20102920"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s11760-013-0593-4","article-title":"Biometric authentication based on PCG and ECG signals: Present status and future directions","volume":"8","author":"Ahmed","year":"2014","journal-title":"Signal Image Video Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cmpb.2019.02.009","article-title":"Human identification using a new matching Pursuit-based feature set of ECG","volume":"172","author":"Goshvarpour","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TII.2018.2874462","article-title":"Heartrate-Dependent Heartwave Biometric Identification with Thresholding-Based GMM\u2013HMM Methodology","volume":"15","author":"Lim","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"18251","DOI":"10.1109\/ACCESS.2018.2820684","article-title":"A Multiscale Autoregressive Model-Based Electrocardiogram Identification Method","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, P., Zhang, X., Liu, M., Hu, X., Pang, B., Yao, Z., Jiang, H., and Chen, H. (2016, January 17\u201319). A 410-nW efficient QRS processor for mobile ECG monitoring in 0.18-\u03bcm CMOS. Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), Shanghai, China.","DOI":"10.1109\/BioCAS.2016.7833713"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, J.N., Byeon, Y.H., Pan, S.B., and Kwak, K.C. (2018). An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal. Sensors, 18.","DOI":"10.3390\/s18114024"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1109\/JBHI.2018.2792404","article-title":"Fast QRS Detection and ECG Compression Based on Signal Structural Analysis","volume":"23","author":"Burguera","year":"2019","journal-title":"IEEE J. Biomed. Health"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, W., Kim, S., and Kim, D. (2018). Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns. Sensors, 18.","DOI":"10.3390\/s18041005"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/JBHI.2017.2686436","article-title":"Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring","volume":"22","author":"Satija","year":"2018","journal-title":"IEEE J. Biomed. Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"34060","DOI":"10.1109\/ACCESS.2019.2900719","article-title":"Noise Rejection for Wearable ECGs Using Modeified Frequency Slice Wavelet Transform and Convolutional Neural Networks","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/JBHI.2018.2842919","article-title":"Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features From Single-Lead ECG Signal","volume":"23","author":"Zarei","year":"2019","journal-title":"IEEE J. Biomed. Health"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11805","DOI":"10.1109\/ACCESS.2017.2707460","article-title":"HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jung, W.-H., and Lee, S.-G. (2017). ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method. Appl. Sci., 7.","DOI":"10.3390\/app7111205"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, B.H., and Pyun, J.Y. (2020). ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks. Sensors, 20.","DOI":"10.3390\/s20113069"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"92871","DOI":"10.1109\/ACCESS.2019.2928017","article-title":"ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"51598","DOI":"10.1109\/ACCESS.2019.2912519","article-title":"ECG Authentication Method Based on Parallel Multi-Scale One-Dimensional Residual Network with Center and Margin Loss","volume":"7","author":"Chu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TSMC.2014.2336842","article-title":"ECG Biometric with Abnormal Cardiac Conditions in Remote Monitoring System","volume":"4","author":"Sidek","year":"2014","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/JBHI.2015.2402199","article-title":"Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition","volume":"20","author":"Gutta","year":"2016","journal-title":"IEEE J. Biomed. Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"168669","DOI":"10.1109\/ACCESS.2019.2954576","article-title":"An Enhanced Machine Learning-Based Biometric Authentication System Using RR-Interval Framed Electrocardiograms","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, X., Diao, M., Hu, Z., Hu, X., Gao, Y., and Huang, R. (2018, January 25\u201329). QoE Prediction for IPTV Based on Imbalanced Dataset by the PNN-PSO algorithm. Proceedings of the 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC.2018.8450530"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.sab.2019.02.007","article-title":"A hybrid variable selection method based on wavelet transform and mean impact value for calorific value determination of coal using laser-induced breakdown spectroscopy and kernel extreme learning machine","volume":"154","author":"Yan","year":"2019","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.applthermaleng.2015.10.104","article-title":"Research on a feature selection method based on median impact value for modeling in thermal power plants","volume":"94","author":"Qi","year":"2016","journal-title":"Appl. Therm. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6168","DOI":"10.1109\/ACCESS.2017.2695498","article-title":"Levy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization","volume":"5","author":"Ling","year":"2017","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13062","DOI":"10.1109\/ACCESS.2017.2723610","article-title":"Elman Neural Network Soft-Sensor Model of Conversion Velocity in Polymerization Process Optimized by Chaos Whale Optimization Algorithm","volume":"5","author":"Sun","year":"2017","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1049\/iet-rpg.2018.5317","article-title":"Parameters estimation of single- and multiple-diode photovoltaic model using whale optimisation algorithm","volume":"12","author":"Elazab","year":"2018","journal-title":"IET Renew. Power Gener."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Debnath, L. (2002). Wavelet Transforms and Their Applications, Birkh\u00e4user.","DOI":"10.1007\/978-1-4612-0097-0"},{"key":"ref_32","unstructured":"Emeritus, D.S. (2011). The Wavelet Transform, Springer."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The Whale Optimization Algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_34","unstructured":"(2021, January 03). Available online: www.physionet.org."},{"key":"ref_35","unstructured":"Lugovaya, T.S. (2005). Biometric Human Identification Based on Electrocardiogram. [Master\u2019s Thesis, Faculty of Computing Technologies and Informatics, Electrotechnical University \u201cLETI\u201d]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH Arrhythmia Database","volume":"20","author":"Moody","year":"2001","journal-title":"IEEE Eng. Med. Biol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dar, M.N., Akram, M.U., Usman, A., and Khan, S.A. (2015, January 15\u201317). ECG Biometric Identification for General Population Using Multiresolution Analysis of DWT Based Features. Proceedings of the 2015 2nd International Conference on Information Security and Cyber Forensics (InfoSec 2015), Cape Town, South Africa.","DOI":"10.1109\/InfoSec.2015.7435498"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dar, M.N., Akram, M.U., Shaukat, A., and Khan, M.A. (2015, January 24\u201327). ECG Based Biometric Identification for Population with Normal and Cardiac Anomalies Using Hybrid HRV and DWT Features. Proceedings of the 2015 5th International Conference on IT Convergence and Security (ICITCS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICITCS.2015.7292977"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sarkar, A., Abbott, A.L., and Doerzaph, Z. (2015, January 8\u201311). ECG Biometric Authentication Using a Dynamical Model. Proceedings of the 7th International Conference on Biometric Theory, Applications and Systems (BTAS), Arlington, VA, USA.","DOI":"10.1109\/BTAS.2015.7358757"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, D., Si, Y.J., Yang, W.Y., Zhang, G., and Li, J. (2019). A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding. Electronics, 8.","DOI":"10.3390\/electronics8060667"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"40078","DOI":"10.1109\/ACCESS.2019.2903575","article-title":"ECG-Based Advanced Personal Identification Study with Adjusted (Qi (*) Si)","volume":"7","author":"Ko","year":"2019","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, N., Zhu, L., Ma, W., Wang, Y., He, F., Zheng, A., and Zhang, X. (2022). The Identification of ECG Signals Using WT-UKF and IPSO-SVM. Sensors, 22.","DOI":"10.3390\/s22051962"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:25:56Z","timestamp":1760138756000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,8]]},"references-count":43,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22124343"],"URL":"https:\/\/doi.org\/10.3390\/s22124343","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,8]]}}}