{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:36:01Z","timestamp":1778754961409,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T00:00:00Z","timestamp":1647216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National\u00a0Natural\u00a0Science\u00a0Foundation\u00a0of\u00a0China","award":["32070671"],"award-info":[{"award-number":["32070671"]}]},{"name":"Anhui\u00a0Provincial\u00a0Universities\u00a0Excellent\u00a0Topnotch\u00a0Talents\u00a0Training\u00a0Program","award":["gxyq2021200"],"award-info":[{"award-number":["gxyq2021200"]}]},{"name":"Anhui\u00a0 Science\u00a0and\u00a0Technology\u00a0University\u00a0Stabilization\u00a0and\u00a0Introduction\u00a0of\u00a0Talents","award":["XWWD202101"],"award-info":[{"award-number":["XWWD202101"]}]},{"name":"Scientific research project of Anhui University of Science and Technology","award":["2021zryb27"],"award-info":[{"award-number":["2021zryb27"]}]},{"name":"the University Student Innovation and Entrepreneurship","award":["202010879032"],"award-info":[{"award-number":["202010879032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples\u2019 lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart\u2019s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients.<\/jats:p>","DOI":"10.3390\/sym14030571","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T02:56:20Z","timestamp":1647312980000},"page":"571","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal"],"prefix":"10.3390","volume":"14","author":[{"given":"Manhong","family":"Shi","sequence":"first","affiliation":[{"name":"College of Information and Network Engineering, Anhui Science and Technology University, Chuzhou 233100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information and Network Engineering, Anhui Science and Technology University, Chuzhou 233100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Network Engineering, Anhui Science and Technology University, Chuzhou 233100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.asoc.2016.02.049","article-title":"Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index","volume":"43","author":"Fujita","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ebrahimzadeh, E., Pooyan, M., and Bijar, A. (2014). A Novel Approach to Predict Sudden Cardiac Death (SCD) Using Nonlinear and Time-Frequency Analyses from HRV Signals. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0081896"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1093\/eurheartj\/eht356","article-title":"Cardiovascular disease in Europe: Epidemiological update","volume":"34","author":"Nichols","year":"2013","journal-title":"Eur. Heart J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1161\/CIRCULATIONAHA.112.128413","article-title":"Estimating Deaths from Cardiovascular Disease: A Review of Global Methodologies of Mortality Measurement","volume":"127","author":"Pagidipati","year":"2013","journal-title":"Circulation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1161\/CIRCULATIONAHA.111.023879","article-title":"Predicting the future: Risk stratification for sudden cardiac death in patients with left ventricular dysfunction","volume":"125","author":"Passman","year":"2012","journal-title":"Circulation"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shen, T.W., Shen, H.P., Lin, C., and Ou, Y.L. (2007, January 26\u201329). Detection and Prediction of Sudden Cardiac Death (SCD) For Personal Healthcare. Proceedings of the 29th Annual International Conference of the IEEE, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352855"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1101\/sqb.2017.82.034272","article-title":"Symmetry from Asymmetry or Asymmetry from Symmetry?","volume":"82","author":"Kahney","year":"2018","journal-title":"Cold Spring Harb. Symp. Quant. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ins.2017.06.027","article-title":"Application of Deep Convolutional Neural Network for Automated Detection of Myocardial Infarction Using ECG Signals","volume":"415","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2016.10.013","article-title":"Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study","volume":"377","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1161\/01.CIR.0000077519.18416.43","article-title":"Prediction of sudden cardiac death: Appraisal of the studies and methods assessing the risk of sudden arrhythmic death","volume":"108","author":"Huikuri","year":"2003","journal-title":"Circulation"},{"key":"ref_11","unstructured":"VanHoogenhuyze, D., Martin, G., Weiss, J., Schaad, J., and Singer, D. (1989). Spectrum of heart rate variability. Proc. Comput. Cardiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"699","DOI":"10.4236\/jbise.2011.411087","article-title":"Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals","volume":"11","author":"Ebrahimzadeh","year":"2011","journal-title":"Biomed. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/S0008-6363(96)00008-9","article-title":"The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death","volume":"31","author":"Voss","year":"1996","journal-title":"Cardiovasc. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Acharya, U.R., Fujita, H., Vidya, K.S., Ghista, D.N., Lim, W.J.E., and Koh, J.E.W. (2015, January 9\u201312). Automated prediction of sudden cardiac death risk using kolmogorov complexity and recurrence quantification analysis features extracted from HRV signals. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hongkong, China.","DOI":"10.1109\/SMC.2015.199"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3389\/fphys.2019.00809","article-title":"Renyi distribution entropy analysis of short-term heart rate variability signals and its application in coronary artery disease detection","volume":"10","author":"Shi","year":"2019","journal-title":"Front. Physiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1166\/jmihi.2014.1287","article-title":"Machine Learning Approach for Sudden Cardiac Arrest Prediction Based on Optimal Heart Rate Variability Features","volume":"4","author":"Murukesan","year":"2014","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_17","unstructured":"Mirhoseini, S.R., Jahedmotlagh, M.R., and Pooyan, M. (2016, January 20\u201322). Improve Accuracy of Early Detection Sudden Cardiac Deaths (SCD) Using Decision Forest and SVM. Proceedings of the International Conference on Robotics and Artificial Intelligence (ICRAI2016), Los Angeles, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1007\/s11517-017-1764-1","article-title":"A time local subset feature selection for prediction of sudden cardiac death from ECG signal","volume":"56","author":"Ebrahimzadeh","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.cmpb.2018.12.001","article-title":"An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal","volume":"169","author":"Ebrahimzadeh","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"118","DOI":"10.3389\/fphys.2020.00118","article-title":"Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals","volume":"11","author":"Shi","year":"2020","journal-title":"Front. Physiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.knosys.2015.03.015","article-title":"An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features","volume":"83","author":"Acharya","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_22","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_23","first-page":"230","article-title":"A Real-Time QRS Detection Algorithm","volume":"3","author":"Pan","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1847968","DOI":"10.1155\/2018\/1847968","article-title":"Classification of Lightning and Faults in Transmission Line Systems Using Discrete Wavelet Transform","volume":"2018","author":"Chiradeja","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_25","first-page":"6523872","article-title":"Application of Discrete Wavelet Transform in Shapelet-Based Classification","volume":"1","author":"Yan","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1740041","DOI":"10.1142\/S0217984917400413","article-title":"Face recognition algorithm based on Gabor wavelet and locality preserving projections","volume":"31","author":"Liu","year":"2017","journal-title":"Mod. Phys. Lett. B"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.neucom.2018.08.008","article-title":"An improved locality preserving projection with l(1)-norm minimization for dimensionality reduction","volume":"316","author":"Yu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.3390\/sym2031591","article-title":"Asymmetry, Symmetry and Beauty","volume":"2","author":"Kopra","year":"2010","journal-title":"Symmetry"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Desai, K.D., and Sankhe, M.S. (2012, January 16\u201318). A real-time fetal ECG feature extraction using multiscale discrete wavelet transform. Proceedings of the International Conference on Biomedical Engineering & Informatics, Chongqing, China.","DOI":"10.1109\/BMEI.2012.6512966"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1093\/eurheartj\/ehi067","article-title":"Different spectral components of 24 h heart rate variability are related to different modes of death in chronic heart failure","volume":"26","author":"Guzzetti","year":"2005","journal-title":"Eur. Heart J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11517-014-1216-0","article-title":"Assessing the complexity of short-term heartbeat interval series by distribution entropy","volume":"53","author":"Li","year":"2015","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-981-10-6041-0_2","article-title":"Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare","volume":"1028","author":"Bai","year":"2017","journal-title":"Adv. Exp. Med. Biol."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/3\/571\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:36:02Z","timestamp":1760135762000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/3\/571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,14]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["sym14030571"],"URL":"https:\/\/doi.org\/10.3390\/sym14030571","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,14]]}}}