{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T07:56:53Z","timestamp":1759564613361,"version":"3.37.3"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s11042-021-10945-6","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T21:03:40Z","timestamp":1619471020000},"page":"30523-30537","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Classification of cardiac arrhythmias using Zhao-Atlas-Marks time-frequency distribution"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2303-5885","authenticated-orcid":false,"given":"Fulya","family":"Akdeniz","sequence":"first","affiliation":[]},{"given":"\u0130lknur","family":"Kayikcioglu","sequence":"additional","affiliation":[]},{"given":"Temel","family":"Kayikcioglu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"issue":"7","key":"10945_CR1","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1007\/s11760-019-01479-4","volume":"13","author":"FY Abdalla","year":"2019","unstructured":"Abdalla FY, Wu L, Ullah H, Ren G, Noor A, Zhao Y (2019) ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition. SIViP 13(7):1283\u20131291","journal-title":"SIViP"},{"key":"10945_CR2","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, Tan JH, Adam M, Gertych A, San Tan R (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389\u2013396","journal-title":"Comput Biol Med"},{"key":"10945_CR3","doi-asserted-by":"crossref","unstructured":"Akdeniz F, Kayik\u00e7io\u011flu \u0130, Kaya \u0130, Kayik\u00e7io\u011flu T (2016) Using Wigner-Ville distribution in ECG arrhythmia detection for telemedicine applications. In: 2016 39th international conference on telecommunications and signal processing (TSP), pp. 409-412. IEEE.","DOI":"10.1109\/TSP.2016.7760908"},{"issue":"1","key":"10945_CR4","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s13246-019-00722-z","volume":"42","author":"AM Alqudah","year":"2019","unstructured":"Alqudah AM, Albadarneh A, Abu-Qasmieh I, Alquran H (2019) Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features. Austr phys eng sci med 42(1):149\u2013157","journal-title":"Austr phys eng sci med"},{"key":"10945_CR5","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.cmpb.2018.04.021","volume":"161","author":"P Amorim","year":"2018","unstructured":"Amorim P, Moraes T, Fazanaro D, Silva J, Pedrini H (2018) Shearlet and contourlet transforms for analysis of electrocardiogram signals. Comput Methods Prog Biomed 161:125\u2013132","journal-title":"Comput Methods Prog Biomed"},{"issue":"11","key":"10945_CR6","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1109\/LSP.2002.805118","volume":"9","author":"MJ Bastiaans","year":"2002","unstructured":"Bastiaans MJ, Alieva T, Stankovic L (2002) On rotated time-frequency kernels. IEEE Signal Process Lett 9(11):378\u2013381","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"10945_CR7","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1007\/s10916-010-9551-7","volume":"36","author":"R Benali","year":"2012","unstructured":"Benali R, Reguig FB, Slimane ZH (2012) Automatic classification of heartbeats using wavelet neural network. J Med Syst 36(2):883\u2013892","journal-title":"J Med Syst"},{"key":"10945_CR8","doi-asserted-by":"crossref","unstructured":"Chiu CY, Verma B (2013). Relationship between data size, accuracy, diversity and clusters in neural network ensembles. International journal of computational intelligence and applications, 12(04), 1340005.][","DOI":"10.1142\/S1469026813400051"},{"key":"10945_CR9","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1590\/2446-4740.05815","volume":"32","author":"RF Dalvi","year":"2017","unstructured":"Dalvi RF, Zago G, Andre\u00e3o RV (2017) Heartbeat classification system based on neural networks and dimensionality reduction. Res Biomed Eng 32:318\u2013326","journal-title":"Res Biomed Eng"},{"issue":"10","key":"10945_CR10","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1109\/TIM.2010.2057652","volume":"59","author":"C De Capua","year":"2010","unstructured":"De Capua C, Meduri A, Morello R (2010) A smart ECG measurement system based on web-service-oriented architecture for telemedicine applications. IEEE Trans Instrum Meas 59(10):2530\u20132538","journal-title":"IEEE Trans Instrum Meas"},{"issue":"13","key":"10945_CR11","doi-asserted-by":"publisher","first-page":"17555","DOI":"10.1007\/s11042-019-7152-0","volume":"78","author":"SA El-Rahman","year":"2019","unstructured":"El-Rahman SA (2019) Biometric human recognition system based on ECG. Multimed Tools Appl 78(13):17555\u201317572","journal-title":"Multimed Tools Appl"},{"issue":"15","key":"10945_CR12","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1016\/j.patrec.2004.06.014","volume":"25","author":"M Engin","year":"2004","unstructured":"Engin M (2004) ECG beat classification using neuro-fuzzy network. Pattern Recogn Lett 25(15):1715\u20131722","journal-title":"Pattern Recogn Lett"},{"issue":"7","key":"10945_CR13","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1109\/78.298277","volume":"42","author":"Z Guo","year":"1994","unstructured":"Guo Z, Durand LG, Lee HC (1994) The time-frequency distributions of nonstationary signals based on a Bessel kernel. IEEE Trans Signal Process 42(7):1700\u20131707","journal-title":"IEEE Trans Signal Process"},{"issue":"2","key":"10945_CR14","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1109\/T-AFFC.2013.6","volume":"4","author":"SK Hadjidimitriou","year":"2013","unstructured":"Hadjidimitriou SK, Hadjileontiadis LJ (2013) EEG-based classification of music appraisal responses using time-frequency analysis and familiarity ratings. IEEE Trans Affect Comput 4(2):161\u2013172","journal-title":"IEEE Trans Affect Comput"},{"issue":"2","key":"10945_CR15","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/79.127284","volume":"9","author":"F Hlawatsch","year":"1992","unstructured":"Hlawatsch F, Boudreaux-Bartels GF (1992) Linear and quadratic time-frequency signal representations. IEEE Signal Process Mag 9(2):21\u201367","journal-title":"IEEE Signal Process Mag"},{"issue":"4","key":"10945_CR16","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1109\/TIM.2019.2910342","volume":"69","author":"B Hou","year":"2019","unstructured":"Hou B, Yang J, Wang P, Yan R (2019) LSTM-based auto-encoder model for ECG arrhythmias classification. IEEE Trans Instrum Meas 69(4):1232\u20131240","journal-title":"IEEE Trans Instrum Meas"},{"key":"10945_CR17","unstructured":"https:\/\/www.physionet.org\/, Accessed 26 April 2018."},{"issue":"3","key":"10945_CR18","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1007\/s10916-010-9585-x","volume":"36","author":"HF Huang","year":"2012","unstructured":"Huang HF, Hu GS, Zhu L (2012) Sparse representation-based heartbeat classification using independent component analysis. J Med Syst 36(3):1235\u20131247","journal-title":"J Med Syst"},{"issue":"1","key":"10945_CR19","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s10916-017-0871-8","volume":"42","author":"AF Hussein","year":"2018","unstructured":"Hussein AF, Hashim SJ, Aziz AFA, Rokhani FZ, Adnan WAW (2018) Performance evaluation of time-frequency distributions for ECG signal analysis. J Med Syst 42(1):15","journal-title":"J Med Syst"},{"issue":"6","key":"10945_CR20","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1007\/s00521-011-0572-z","volume":"21","author":"S Karpagachelvi","year":"2012","unstructured":"Karpagachelvi S, Arthanari M, Sivakumar M (2012) Classification of electrocardiogram signals with support vector machines and extreme learning machine. Neural Comput & Applic 21(6):1331\u20131339","journal-title":"Neural Comput & Applic"},{"issue":"3","key":"10945_CR21","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2015","unstructured":"Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63(3):664\u2013675","journal-title":"IEEE Trans Biomed Eng"},{"key":"10945_CR22","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.proeng.2016.05.136","volume":"144","author":"A Krishnakumari","year":"2016","unstructured":"Krishnakumari A, Saravanan M, Venkatesan G, Jain S (2016) Application of Zhao-Atlas-Marks transforms in non-stationary bearing fault diagnosis. Proced Eng 144:297\u2013304","journal-title":"Proced Eng"},{"key":"10945_CR23","doi-asserted-by":"crossref","unstructured":"Lin, C. C., Yang, C. M. (2014). Heartbeat classification using normalized RR intervals and wavelet features. In 2014 international symposium on computer, consumer and control (pp. 650-653). IEEE.","DOI":"10.1109\/IS3C.2014.175"},{"key":"10945_CR24","doi-asserted-by":"crossref","unstructured":"Lin CC, Yang CM (2014). Heartbeat classification using normalized RR intervals and morphological features Mathematical Problems in Engineering, 2014.","DOI":"10.1109\/IS3C.2014.175"},{"issue":"9","key":"10945_CR25","doi-asserted-by":"publisher","first-page":"3561","DOI":"10.1016\/j.eswa.2012.12.063","volume":"40","author":"EJDS Luz","year":"2013","unstructured":"Luz EJDS, Nunes TM, De Albuquerque VHC, Papa JP, Menotti D (2013) ECG arrhythmia classification based on optimum-path forest. Expert Syst Appl 40(9):3561\u20133573","journal-title":"Expert Syst Appl"},{"key":"10945_CR26","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.cmpb.2015.12.008","volume":"127","author":"EJDS Luz","year":"2016","unstructured":"Luz EJDS, Schwartz WR, C\u00e1mara-Ch\u00e1vez G, Menotti D (2016) ECG-based heartbeat classification for arrhythmia detection: A survey. Comput Methods Prog Biomed 127:144\u2013164","journal-title":"Comput Methods Prog Biomed"},{"issue":"4","key":"10945_CR27","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1088\/0967-3334\/37\/4\/530","volume":"37","author":"A Mert","year":"2016","unstructured":"Mert A (2016) ECG feature extraction based on the bandwidth properties of variational mode decomposition. Physiol Meas 37(4):530\u2013543","journal-title":"Physiol Meas"},{"issue":"24","key":"10945_CR28","doi-asserted-by":"publisher","first-page":"35351","DOI":"10.1007\/s11042-019-08132-9","volume":"78","author":"K Muthuvel","year":"2019","unstructured":"Muthuvel K, Anto S, Alexander TJ (2019) GABC based neuro-fuzzy classifier with hybrid features for ECG beat classification. Multimed Tools Appl 78(24):35351\u201335372","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"10945_CR29","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/s00034-019-01196-w","volume":"39","author":"NMM Nascimento","year":"2020","unstructured":"Nascimento NMM, Marinho LB, Peixoto SA, do Vale Madeiro, J. P., de Albuquerque, V. H. C., & Rebou\u00e7as Filho, P. P. (2020) Heart arrhythmia classification based on statistical moments and structural co-occurrence. Circuits Syst Signal Process 39(2):631\u2013650","journal-title":"Circuits Syst Signal Process"},{"issue":"14","key":"10945_CR30","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.3390\/app9142921","volume":"9","author":"S Nurmaini","year":"2019","unstructured":"Nurmaini S, Umi Partan R, Caesarendra W, Dewi T, Naufal Rahmatullah M, Darmawahyuni A, Bhayyu V, Firdaus F (2019) An automated ECG beat classification system using deep neural networks with an unsupervised feature extraction technique. Appl Sci 9(14):2921","journal-title":"Appl Sci"},{"key":"10945_CR31","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.compbiomed.2018.06.002","volume":"102","author":"SL Oh","year":"2018","unstructured":"Oh SL, Ng EY, San Tan R, Acharya UR (2018) Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med 102:278\u2013287","journal-title":"Comput Biol Med"},{"issue":"5","key":"10945_CR32","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","volume":"48","author":"M Pal","year":"2010","unstructured":"Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48(5):2297\u20132307","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"17","key":"10945_CR33","doi-asserted-by":"publisher","first-page":"21905","DOI":"10.1007\/s11042-017-5225-5","volume":"77","author":"G Pan","year":"2018","unstructured":"Pan G, Xin Z, Shi S, Jin D (2018) Arrhythmia classification based on wavelet transformation and random forests. Multimed Tools Appl 77(17):21905\u201321922","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"10945_CR34","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10916-016-0660-9","volume":"41","author":"J Park","year":"2017","unstructured":"Park J, Kang M, Gao J, Kim Y, Kang K (2017) Cascade classification with adaptive feature extraction for arrhythmia detection. J Med Syst 41(1):11","journal-title":"J Med Syst"},{"issue":"4","key":"10945_CR35","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s13534-017-0043-2","volume":"7","author":"SS Qurraie","year":"2017","unstructured":"Qurraie SS, Afkhami RG (2017) ECG arrhythmia classification using time frequency distribution techniques. Biomed Eng Lett 7(4):325\u2013332","journal-title":"Biomed Eng Lett"},{"issue":"9","key":"10945_CR36","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 (2013) ECG signal processing for abnormalities detection using multi-resolution wavelet transform and artificial neural network classifier. Measurement 46(9):3238\u20133246","journal-title":"Measurement"},{"key":"10945_CR37","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.compbiomed.2017.06.006","volume":"87","author":"KN Rajesh","year":"2017","unstructured":"Rajesh KN, Dhuli R (2017) Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput Biol Med 87:271\u2013284","journal-title":"Comput Biol Med"},{"key":"10945_CR38","unstructured":"Rashkovska A, Toma\u0161i\u0107 I, Trobec R (2011) A telemedicine application: ECG data from wireless body sensors on a smartphone. In: 2011 proceedings of the 34th international convention MIPRO (pp. 262-265). IEEE."},{"issue":"6","key":"10945_CR39","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.3390\/s17061360","volume":"17","author":"J Son","year":"2017","unstructured":"Son J, Park J, Oh H, Bhuiyan MZA, Hur J, Kang K (2017) Privacy-preserving electrocardiogram monitoring for intelligent arrhythmia detection. Sensors 17(6):1360","journal-title":"Sensors"},{"key":"10945_CR40","doi-asserted-by":"crossref","unstructured":"Trochidis A, Hadjileontiadis L, Zacharias K (2014) Analysis of vibroacoustic modulations for crack detection: a time-frequency approach based on zhao-atlas-marks distribution Shock and Vibration, 2014.","DOI":"10.1155\/2014\/102157"},{"issue":"8","key":"10945_CR41","doi-asserted-by":"publisher","first-page":"10365","DOI":"10.1007\/s11042-018-5762-6","volume":"77","author":"C Venkatesan","year":"2018","unstructured":"Venkatesan C, Karthigaikumar P, Varatharajan RJMT (2018) A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection. Multimed Tools Appl 77(8):10365\u201310374","journal-title":"Multimed Tools Appl"},{"key":"10945_CR42","unstructured":"World Health Organization. (2018). World health statistics 2018: monitoring health for the SDGs, sustainable development goals."},{"issue":"1","key":"10945_CR43","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.eswa.2011.07.101","volume":"39","author":"YC Yeh","year":"2012","unstructured":"Yeh YC, Chiou CW, Lin HJ (2012) Analyzing ECG for cardiac arrhythmia using cluster analysis. Expert Syst Appl 39(1):1000\u20131010","journal-title":"Expert Syst Appl"},{"key":"10945_CR44","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.cmpb.2019.05.004","volume":"176","author":"O Yildirim","year":"2019","unstructured":"Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR (2019) A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Methods Prog Biomed 176:121\u2013133","journal-title":"Comput Methods Prog Biomed"},{"issue":"7","key":"10945_CR45","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/29.57537","volume":"38","author":"Y Zhao","year":"1990","unstructured":"Zhao Y, Atlas LE, Marks RJ (1990) The use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals. IEEE Trans Acoust Speech Signal Process 38(7):1084\u20131091","journal-title":"IEEE Trans Acoust Speech Signal Process"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10945-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10945-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10945-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T05:47:53Z","timestamp":1631684873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10945-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,26]]},"references-count":45,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["10945"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10945-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,4,26]]},"assertion":[{"value":"19 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors claim no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}