{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:46:15Z","timestamp":1740181575535,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-02718-3","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T16:02:24Z","timestamp":1711641744000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decision Support System for Predicting Ventricular Arrhythmias Using Non-linear Features of ECG Signals"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0488-8989","authenticated-orcid":false,"given":"Monalisa","family":"Mohanty","sequence":"first","affiliation":[]},{"given":"Pratyusa","family":"Dash","sequence":"additional","affiliation":[]},{"given":"Sukant","family":"Sabut","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"issue":"6","key":"2718_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/51.731315","volume":"17","author":"H Dirk","year":"1998","unstructured":"Dirk H, Bernd P, Hanspeter H, et al. Non-linear coordination of cardiovascular automatic control. IEEE Eng Med Biol. 1998;17(6):17\u201321.","journal-title":"IEEE Eng Med Biol"},{"key":"2718_CR2","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.jelectrocard.2012.11.010","volume":"46","author":"PJ Leisy","year":"2013","unstructured":"Leisy PJ, Coeytaux RR, Wagner GS, et al. ECG-based signal analysis technologies for evaluating patients with acute coronary syndrome: a systematic review. J Electrocardiol. 2013;46:92\u20137.","journal-title":"J Electrocardiol"},{"key":"2718_CR3","first-page":"S27","volume":"20","author":"J Brugada","year":"2002","unstructured":"Brugada J. Relevance of atrial fibrillation classification in clinical practice. J Cardiovasc Electrophysiol. 2002;20:S27\u201330.","journal-title":"J Cardiovasc Electrophysiol"},{"key":"2718_CR4","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/1475-925X-4-6","volume":"4","author":"Y Sun","year":"2005","unstructured":"Sun Y, Chan KL, Krishnan SM. Life-threatening ventricular arrhythmia recognition by nonlinear descriptor. Biomed Eng Online. 2005;4:6.","journal-title":"Biomed Eng Online"},{"issue":"1","key":"2718_CR5","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1002\/ima.22477","volume":"31","author":"P Mathivanan","year":"2020","unstructured":"Mathivanan P, Ganesh AB. ECG steganography based on tunable Q-factor wavelet transform and singular value decomposition. Int J Imaging Syst Technol. 2020;31(1):270\u201387.","journal-title":"Int J Imaging Syst Technol"},{"key":"2718_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-009-0136-1","volume":"5","author":"MA Arafat","year":"2011","unstructured":"Arafat MA, Chowdhury AW, Hasan MK. A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram. SIViP. 2011;5:1\u201310.","journal-title":"SIViP"},{"issue":"7","key":"2718_CR7","doi-asserted-by":"publisher","first-page":"2476","DOI":"10.1016\/j.eswa.2012.10.054","volume":"40","author":"PS Wasan","year":"2013","unstructured":"Wasan PS, Uttamchandani M, Moochhala S, et al. Application of statistics and machine learning for risk stratification of heritable cardiac arrhythmias. Expert Syst Appl. 2013;40(7):2476\u201386.","journal-title":"Expert Syst Appl"},{"key":"2718_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fphy.2019.00001","volume":"7","author":"M Miquel Alfaras","year":"2019","unstructured":"Miquel Alfaras M, Soriano MC, Ort\u00edn S. A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection. Front Phys. 2019;7:1\u201311.","journal-title":"Front Phys"},{"key":"2718_CR9","doi-asserted-by":"crossref","unstructured":"Sahoo S, Das T, Sabut S. Adaptive thresholding based EMD for delineation of QRS complex in ECG signal analysis. In: Int. Conf. Wireless Comm. Sig. Proc. and Network. 2016.","DOI":"10.1109\/WiSPNET.2016.7566185"},{"key":"2718_CR10","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1016\/j.bbe.2017.04.007","volume":"37","author":"U Maji","year":"2017","unstructured":"Maji U, Mitra M, Pal S. Characterization of cardiac arrhythmias by variational mode decomposition technique. Biocybern Biomed Eng. 2017;37:578\u201389.","journal-title":"Biocybern Biomed Eng"},{"issue":"9","key":"2718_CR11","doi-asserted-by":"publisher","first-page":"3238","DOI":"10.1016\/j.measurement.2013.05.021","volume":"46","author":"HM Rai","year":"2013","unstructured":"Rai HM, Trivedi A, Shukla S. ECG signal processing for abnormalities detection using multi-resolution wavelet transform and artificial neural network classifier. Measurement. 2013;46(9):3238\u201346.","journal-title":"Measurement"},{"key":"2718_CR12","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.bbe.2017.02.002","volume":"37","author":"M Rakshit","year":"2017","unstructured":"Rakshit M, Das S. An efficient wavelet-based automated R-peaks detection method using Hilbert transform. Biocybern Biomed Eng. 2017;37:566\u201377.","journal-title":"Biocybern Biomed Eng"},{"key":"2718_CR13","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.bspc.2015.04.010","volume":"20","author":"M Nazarahari","year":"2015","unstructured":"Nazarahari M, Namin SG, Hossein A, et al. A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification. Biomed Signal Process Control. 2015;20:142\u201351.","journal-title":"Biomed Signal Process Control"},{"key":"2718_CR14","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jelectrocard.2019.12.009","volume":"59","author":"K Zhang","year":"2020","unstructured":"Zhang K, Aleexenko V, Jeevaratnam K. Computational approaches for detection of cardiac rhythm abnormalities: are we there yet? J Electrocardiol. 2020;59:28\u201334.","journal-title":"J Electrocardiol"},{"key":"2718_CR15","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.bspc.2014.04.001","volume":"13","author":"RJ Martis","year":"2014","unstructured":"Martis RJ, Acharya UR, et al. Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation. Biomed Signal Process Control. 2014;13:295\u2013305.","journal-title":"Biomed Signal Process Control"},{"key":"2718_CR16","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.cmpb.2016.04.009","volume":"131","author":"M Garcia","year":"2016","unstructured":"Garcia M, Rodenas J, Alcaraz R, Rieta JJ. Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation. Comput Methods Programs Biomed. 2016;131:157\u201368.","journal-title":"Comput Methods Programs Biomed"},{"key":"2718_CR17","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.compbiomed.2015.03.005","volume":"60","author":"S Asgari","year":"2015","unstructured":"Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med. 2015;60:132\u201342.","journal-title":"Comput Biol Med"},{"key":"2718_CR18","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1016\/j.compbiomed.2009.08.007","volume":"39","author":"MA Arafat","year":"2009","unstructured":"Arafat MA, Sieed J, Hasan MK. Detection of ventricular fibrillation using empirical mode decomposition and Bayes decision theory. Comput Biol Med. 2009;39:1051\u20137.","journal-title":"Comput Biol Med"},{"key":"2718_CR19","first-page":"260","volume":"3","author":"L Kaur","year":"2013","unstructured":"Kaur L, Singh V. Ventricular fibrillation detection using empirical mode decomposition and approximate entropy. Int J Emerg Technol Adv Eng. 2013;3:260\u20138.","journal-title":"Int J Emerg Technol Adv Eng"},{"key":"2718_CR20","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.bspc.2017.07.031","volume":"39","author":"Y Xu","year":"2018","unstructured":"Xu Y, Wang D, Zhang W, et al. Detection of ventricular tachycardia and fibrillation using adaptive variational mode decomposition and boosted-CART classifier. Biomed Signal Process Control. 2018;39:219\u201329.","journal-title":"Biomed Signal Process Control"},{"key":"2718_CR21","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1007\/s11760-019-01479-4","volume":"13","author":"FO Abdalla","year":"2019","unstructured":"Abdalla FO, Wu L, Ullah H, et al. ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition. Signal Image Video Process. 2019;13:1283\u201391.","journal-title":"Signal Image Video Process"},{"key":"2718_CR22","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.knosys.2015.03.015","volume":"83","author":"UR Acharya","year":"2015","unstructured":"Acharya UR, Fujita H, Sudarshan VK, et al. An integrated index for detection of sudden cardiac death using discrete wavelet transform and nonlinear features. Knowl-Based Syst. 2015;83:149\u201358.","journal-title":"Knowl-Based Syst"},{"key":"2718_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S1793536909000047","volume":"1","author":"W Zhaohua","year":"2009","unstructured":"Zhaohua W, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal. 2009;1:1\u201341.","journal-title":"Adv Adapt Data Anal"},{"key":"2718_CR24","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/s10916-016-0441-5","volume":"40","author":"RK Tripathy","year":"2016","unstructured":"Tripathy RK, Sharma LN, Dandapat S. Detection of shockable ventricular arrhythmia using variational mode decomposition. J Med Syst. 2016;40:40\u201379.","journal-title":"J Med Syst"},{"key":"2718_CR25","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.compbiomed.2017.06.006","volume":"87","author":"PS Rajesh","year":"2017","unstructured":"Rajesh PS, Dhuli R. Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput Biol Med. 2017;87:271\u201384.","journal-title":"Comput Biol Med"},{"key":"2718_CR26","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.bspc.2018.03.014","volume":"44","author":"MT Nguyen","year":"2018","unstructured":"Nguyen MT, Shahzad A, Nguyen BV, Kim K. Diagnosis of shockable rhythms for automated external defibrillators using a reliable support vector machine classifier. Biomed Signal Process Control. 2018;44:258\u201369.","journal-title":"Biomed Signal Process Control"},{"key":"2718_CR27","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.protcy.2013.12.335","volume":"10","author":"U Maji","year":"2013","unstructured":"Maji U, Mitra M, Pal S. Automatic detection of atrial fibrillation using empirical mode decomposition and statistical approach. Procedia Technol. 2013;10:45\u201352.","journal-title":"Procedia Technol"},{"key":"2718_CR28","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","volume":"89","author":"UR Acharya","year":"2017","unstructured":"Acharya UR, Oh SL, Hagiwara Y, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 2017;89:389\u201396.","journal-title":"Comput Biol Med"},{"key":"2718_CR29","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s11517-012-0980-y","volume":"51","author":"K Balasundaram","year":"2013","unstructured":"Balasundaram K, Masse S, Nair K, Umapathy K. A classification scheme for ventricular arrhythmias using wavelets analysis. Med Biol Eng Comput. 2013;51:153\u201364.","journal-title":"Med Biol Eng Comput"},{"issue":"1","key":"2718_CR30","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/1475-925X-9-43","volume":"9","author":"E Anas","year":"2010","unstructured":"Anas E, Lee S, Hasan M. Sequential algorithm for life-threatening cardiac pathologies detection based on mean signal strength and emd functions. Biomed Eng Online. 2010;9(1):43\u201364.","journal-title":"Biomed Eng Online"},{"key":"2718_CR31","unstructured":"Novakovic J. Using information gain attribute evaluation to classify sonar targets. In: Telecom. forum TELFOR. 2009. p. 1351\u20134."},{"key":"2718_CR32","doi-asserted-by":"publisher","first-page":"4625","DOI":"10.1016\/j.eswa.2014.01.017","volume":"41","author":"CJ Mantas","year":"2014","unstructured":"Mantas CJ, Abell\u00e1n J. Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data. Expert Syst Appl. 2014;41:4625\u201337.","journal-title":"Expert Syst Appl"},{"key":"2718_CR33","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.imu.2016.12.003","volume":"6","author":"EJ Ciaccio","year":"2017","unstructured":"Ciaccio EJ, Biviano AB, Iyer V, Garan H. Trends in quantitative methods used for atrial fibrillation and ventricular tachycardia analyses. Inform Med Unlocked. 2017;6:12\u201327.","journal-title":"Inform Med Unlocked"},{"key":"2718_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphys.2018.00722","volume":"9","author":"RK Tripathy","year":"2018","unstructured":"Tripathy RK, Zamora-Mendez A, Serna JA, et al. Detection of life threatening ventricular arrhythmia using digital taylor fourier transform. Front Physiol. 2018;9:1\u201312.","journal-title":"Front Physiol"},{"key":"2718_CR35","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/BF02510970","volume":"35","author":"L Khadra","year":"1997","unstructured":"Khadra L, Al-Fahoum AS, Al-Nashash H. Detection of life-threatening cardiac arrhythmias using the wavelet transformation. Med Biol Eng Comput. 1997;35:626\u201332.","journal-title":"Med Biol Eng Comput"},{"key":"2718_CR36","first-page":"21","volume":"31","author":"SR de Gauna","year":"2004","unstructured":"de Gauna SR, Lazkano A, Ruiz J, Aramendi E. Discrimination between ventricular tachycardia and ventricular fibrillation using the continuous wavelet transform. Comput Cardiol. 2004;31:21\u20134.","journal-title":"Comput Cardiol"},{"key":"2718_CR37","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1002\/ima.22471","volume":"31","author":"W Xiao","year":"2021","unstructured":"Xiao W, Gao Q, Kumar R, et al. Implementation of convolutional neural network categorizers in coronary ischemia detection. Int J Imaging Syst Technol. 2021;31:313\u201326.","journal-title":"Int J Imaging Syst Technol"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02718-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-02718-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02718-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T16:20:59Z","timestamp":1711642859000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-02718-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,28]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["2718"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-02718-3","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2024,3,28]]},"assertion":[{"value":"2 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The paper does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics statement"}}],"article-number":"357"}}