{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:48:16Z","timestamp":1769086096062,"version":"3.49.0"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T00:00:00Z","timestamp":1530230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments\u2014including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet\u2014using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University\u2019s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm\u2019s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets.<\/jats:p>","DOI":"10.3390\/s18072090","type":"journal-article","created":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T10:51:50Z","timestamp":1530269510000},"page":"2090","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals"],"prefix":"10.3390","volume":"18","author":[{"given":"Shirin","family":"Hajeb-Mohammadalipour","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran 1985717443, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohsen","family":"Ahmadi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran 1985717443, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Shahghadami","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran 1985717443, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ki H.","family":"Chon","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,29]]},"reference":[{"key":"ref_1","first-page":"e29","article-title":"Heart disease and stroke statistics\u20142015 Update","volume":"131","author":"Mozaffarian","year":"2015","journal-title":"Circulation"},{"key":"ref_2","unstructured":"Centers for Disease Control and Prevention (2016, November 11). Underlying Cause of Death 1999\u20132014, Available online: https:\/\/wonder.cdc.gov\/wonder\/help\/ucd.html."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sadrawi, M., Lin, C.-H., Lin, Y.-T., Hsieh, Y., Kuo, C.-C., Chien, J.C., Haraikawa, K., Abbod, M.F., and Shieh, J.-S. (2017). Arrhythmia evaluation in Wearable ECG Devices. Sensors, 17.","DOI":"10.3390\/s17112445"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1007\/s10439-009-9740-z","article-title":"Automatic real time detection of atrial fibrillation","volume":"37","author":"Dash","year":"2009","journal-title":"Ann. Biomed Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TBME.2012.2208112","article-title":"Atrial fibrillation detection using an iPhone 4S","volume":"60","author":"Lee","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.camwa.2007.04.035","article-title":"Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier","volume":"55","author":"Lin","year":"2008","journal-title":"Comput. Math. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1088\/0967-3334\/36\/2\/283","article-title":"Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincare plots","volume":"36","author":"Zhang","year":"2015","journal-title":"Physiol. Meas."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.bspc.2010.05.003","article-title":"Chaotic based reconstructed phase space features for detecting ventricular fibrillation","volume":"5","author":"Roopaei","year":"2010","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.cmpb.2015.12.008","article-title":"ECG-based heartbeat classification for arrhythmia detection: A survey","volume":"127","author":"Luz","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","first-page":"578","article-title":"Nonlinear coupling in absence in acute myocardial patients but not healthy subjects","volume":"295","author":"Bai","year":"2008","journal-title":"Am. J. Physiol."},{"key":"ref_11","first-page":"1475","article-title":"Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability","volume":"291","author":"Zhong","year":"2006","journal-title":"Am. J. Physiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1088\/0967-3334\/37\/4\/530","article-title":"ECG feature extraction based on the bandwidth properties of variational mode decomposition","volume":"37","author":"Mert","year":"2016","journal-title":"Physiol. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mert, A. (2016, January 16\u201319). ECG signal analysis based on variational mode decomposition and bandwidth property. Proceedings of the 2016 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey.","DOI":"10.1109\/SIU.2016.7495962"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, H., Yuan, D., Wang, Y., Cui, D., and Cao, L. (2016). Arrhythmia classification based on multi-domain feature extraction for an ECG recognition system. Sensors, 16.","DOI":"10.3390\/s16101744"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","article-title":"Automatic classification of heartbeats using ECG morphology and heartbeat interval features","volume":"51","author":"Reilly","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/10.959322","article-title":"ECG beat recognition using fuzzy hybrid neural network","volume":"48","author":"Osowski","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7563","DOI":"10.1016\/j.eswa.2010.04.087","article-title":"ECG beat classification using particle swarm optimization and radial basis function neural network","volume":"37","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/10.740880","article-title":"Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network","volume":"46","author":"Minami","year":"1999","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/51.376752","article-title":"Detecting ventricular fibrillation","volume":"14","author":"Afonso","year":"1995","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_20","first-page":"355","article-title":"Automatic identification and recording of cardiac arrhythmia","volume":"27","author":"Small","year":"2000","journal-title":"Comput. Cardiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1109\/TBME.2003.820401","article-title":"Nonlinear analysis of the separate contributions of automatic nervous system to heart rate variability using principal dynamic modes","volume":"51","author":"Zhong","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1114\/1.1451074","article-title":"A stochastic nonlinear autoregressive algorithm reflects nonlinear dynamics of heart-rate fluctuations","volume":"30","author":"Armoundas","year":"2002","journal-title":"Ann. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/10.488800","article-title":"A dual-input nonlinear system analysis of autonomic modulation of heart rate","volume":"43","author":"Chon","year":"1996","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2601","DOI":"10.1016\/j.eswa.2007.05.008","article-title":"Adaptive wavelet network for multiple cardiac arrhythmias recognition","volume":"34","author":"Lin","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Salah, H., and Noureddine, E. (2015). Cardiac arrhythmia classification by wavelet transform. Int. J. Adv. Res. Artif. Intell. (IJARAI), 4.","DOI":"10.14569\/IJARAI.2015.040503"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1007\/s10916-016-0467-8","article-title":"Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier","volume":"40","author":"Alickovic","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_27","unstructured":"Lopez, A.D., and Joseph, L.A. (2013, January 19\u201321). Classification of arrhythmias using statistical features in the wavelet transform domain. Proceedings of the 2013 International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/1475-925X-1-5","article-title":"Cardiac arrhythmia classification using autoregressive modeling","volume":"1","author":"Ge","year":"2002","journal-title":"Biomed. Eng. OnLine"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10916-016-0441-5","article-title":"Detection of shockable ventricular arrhythmia using variational mode decomposition","volume":"40","author":"Tripathy","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/1475-925X-4-60","article-title":"Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators","volume":"4","author":"Amann","year":"2005","journal-title":"BioMed. Eng. OnLine"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/10.959330","article-title":"Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?","volume":"48","author":"Brennan","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TBME.2006.880909","article-title":"Detecting ventricular fibrillation by time-delay methods","volume":"54","author":"Amann","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Soille, P. (1999). Morphological Image Analysis: Principles and Applications, Springer.","DOI":"10.1007\/978-3-662-03939-7"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_37","first-page":"227","article-title":"A new method for detecting atrial fibrillation using R-R intervals","volume":"10","author":"Moody","year":"1983","journal-title":"Comput. Cardiol."},{"key":"ref_38","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_39","first-page":"515","article-title":"CREI-GARD, A new concept in computerized arrhythmia monitoring systems","volume":"13","author":"Nolle","year":"1986","journal-title":"Comput. Cardiol."},{"key":"ref_40","unstructured":"Greenwald, S.D. (1986). The Development and Analysis of a Ventricular Fibrillation Detector. [Master\u2019s Thesis, Massachusetts Institute of Technology]."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"259","DOI":"10.4236\/jbise.2016.95019","article-title":"Detection of ventricular fibrillation using random forest classifier","volume":"9","author":"Verma","year":"2016","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1109\/TBME.2013.2275000","article-title":"Ventricular fibrillation and tachycardia classification using a machine learning approach","volume":"61","author":"Li","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.dsp.2015.12.002","article-title":"Effective and efficient detection of premature ventricular contractions based on variation of principal directions","volume":"50","author":"Zarei","year":"2016","journal-title":"Digit. Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1007\/s11760-012-0339-8","article-title":"Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods","volume":"8","author":"Alajlan","year":"2014","journal-title":"Signal Image Video Process"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/10.58594","article-title":"Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm","volume":"37","author":"Thakor","year":"1990","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/BF02447420","article-title":"Ventricular fibrillation detection by a regression test on the autocorrelation function","volume":"25","author":"Chen","year":"1987","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_47","unstructured":"Kuo, S., and Dillman, R. (1978). Computer detection of ventricular fibrillation. IEEE Comput. Cardiol., 347\u2013349."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/0141-5425(89)90067-8","article-title":"Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system","volume":"11","author":"Barro","year":"1989","journal-title":"J. Biomed. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1109\/10.759055","article-title":"Detecting ventricular tachycardia and fibrillation by complexity measure","volume":"46","author":"Zhang","year":"1999","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/TBME.2013.2290800","article-title":"Detection of life-threatening arrhythmias using feature selection and support vector machines","volume":"61","author":"Atienza","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.bspc.2007.01.002","article-title":"Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set","volume":"2","author":"Jekova","year":"2007","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1088\/0967-3334\/25\/5\/007","article-title":"Real time detection of ventricular fibrillation and tachycardia","volume":"25","author":"Jekova","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1088\/0967-3334\/21\/4\/301","article-title":"Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG","volume":"21","author":"Jekova","year":"2000","journal-title":"Physiol. Meas."},{"key":"ref_54","unstructured":"(2016, December 15). Detection of Premature Ventricular Contraction Beats Using ANN. Available online: http:\/\/connection.ebscohost.com\/c\/articles\/82678089\/detection-premature-ventricular-contraction-beats-using-ann."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11760-013-0478-6","article-title":"A low-complexity data-adaptive approach for premature ventricular contraction recognition","volume":"8","author":"Li","year":"2014","journal-title":"Signal Image Video Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/TBME.2011.2175729","article-title":"Automatic motion and noise artifacts detection on Holter ECG data using empirical model decomposition and statistical methods","volume":"59","author":"Lee","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1007\/s10439-014-1080-y","article-title":"Photoplethysmograph signal reconstruction based on a novel hybrid motion artifact dection-reduction approach- Part I: Motion and noise artifact detection","volume":"42","author":"Chong","year":"2014","journal-title":"Ann. Biomed Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2090\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:10:43Z","timestamp":1760195443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,29]]},"references-count":57,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["s18072090"],"URL":"https:\/\/doi.org\/10.3390\/s18072090","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,29]]}}}