{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T09:47:40Z","timestamp":1749548860703,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"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":[[2022,5]]},"DOI":"10.1007\/s11042-022-12614-8","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T02:02:39Z","timestamp":1646186559000},"page":"16047-16065","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Label consistent non-negative representation of ECG signals for automated recognition of cardiac arrhythmias"],"prefix":"10.1007","volume":"81","author":[{"given":"Bing","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jizhong","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4505-0568","authenticated-orcid":false,"given":"Jianhua","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"12614_CR1","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 (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389\u2013396","journal-title":"Comput Biol Med"},{"issue":"1","key":"12614_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2010","unstructured":"Boyd S, Parikh N, Chu E, Peleato B (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1\u2013122","journal-title":"Found Trends Mach Learn"},{"key":"12614_CR3","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2018.02.067","volume":"292","author":"G Cui","year":"2018","unstructured":"Cui G, Li X, Dong Y (2018) Subspace clustering guided convex nonnegative matrix factorization. Neurocomputing 292:38\u201348","journal-title":"Neurocomputing"},{"key":"12614_CR4","volume-title":"12 IEEE Int C Elect Energy Env Communications Computer Control","author":"U Desai","year":"2015","unstructured":"Desai U, Martis RJ, Nayak CG, Sarika K (2015) Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. In: 12 IEEE Int C Elect Energy Env Communications Computer Control"},{"key":"12614_CR5","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.cmpb.2015.12.024","volume":"127","author":"FA Elhaj","year":"2016","unstructured":"Elhaj FA, Salim N, Harris AR, Swee TT (2016) Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Meth Prog Bio 127:52\u201363","journal-title":"Comput Meth Prog Bio"},{"issue":"6","key":"12614_CR6","doi-asserted-by":"publisher","first-page":"1850088","DOI":"10.1142\/S0218126618500883","volume":"27","author":"J Hua","year":"2018","unstructured":"Hua J, Zhang H, Liu J, Xu Y (2018) Direct arrhythmia classification from compressive ECG signals in wearable health monitoring system. J Circuit Syst Comp 27(6):1850088","journal-title":"J Circuit Syst Comp"},{"key":"12614_CR7","doi-asserted-by":"publisher","first-page":"101687","DOI":"10.1016\/j.sysarc.2019.101687","volume":"104","author":"J Hua","year":"2020","unstructured":"Hua J, Xu Y, Tang J, Liu J (2020) ECG heartbeat classification in compressive domain for wearable devices. J Syst Architect 104:101687","journal-title":"J Syst Architect"},{"issue":"3","key":"12614_CR8","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":"11","key":"12614_CR9","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1109\/TPAMI.2013.88","volume":"35","author":"ZL Jiang","year":"2013","unstructured":"Jiang ZL, Lin Z, Davis LS (2013) Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE T Pattern Anal 35(11):2651\u20132664","journal-title":"IEEE T Pattern Anal"},{"issue":"6755","key":"12614_CR10","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"D Lee","year":"1999","unstructured":"Lee D, Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788\u2013791","journal-title":"Nature"},{"key":"12614_CR11","first-page":"1537","volume-title":"IEEE 17th international conference on communication technology (ICCT)","author":"N Li","year":"2017","unstructured":"Li N, Si Y, Deng D, Yuan C (2017) ECG beats classification via online sparse dictionary and time pyramid matching. In: IEEE 17th international conference on communication technology (ICCT), pp 1537\u20131543"},{"key":"12614_CR12","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.patrec.2019.11.005","volume":"129","author":"R Li","year":"2020","unstructured":"Li R, Yang GP, Wang KK, Huang YW (2020) Robust ECG biometrics using GNMF and sparse representation. Pattern Recogn Lett 129:70\u201376","journal-title":"Pattern Recogn Lett"},{"issue":"3","key":"12614_CR13","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/s10618-012-0265-y","volume":"26","author":"HW Liu","year":"2013","unstructured":"Liu HW, Li XL, Zheng XY (2013) Solving non-negative matrix factorization by alternating least squares with a modified strategy. Data Min Knowl Disc 26(3):435\u2013451","journal-title":"Data Min Knowl Disc"},{"issue":"8","key":"12614_CR14","doi-asserted-by":"publisher","first-page":"2168","DOI":"10.1109\/TBME.2011.2113395","volume":"58","author":"T Mar","year":"2011","unstructured":"Mar T, Zaunseder S, Pablo Martinez J (2011) Optimization of ECG classification by means of feature selection. IEEE T Bio-Med Eng 58(8):2168\u20132177","journal-title":"IEEE T Bio-Med Eng"},{"key":"12614_CR15","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.future.2019.03.025","volume":"97","author":"LB Marinho","year":"2019","unstructured":"Marinho LB, Nascimento NDMM, Souza JWM, Gurgel MV (2019) A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Gener Comp Sy 97:564\u2013577","journal-title":"Future Gener Comp Sy"},{"key":"12614_CR16","first-page":"422","volume-title":"Annual IEEE India conference","author":"RJ Martis","year":"2010","unstructured":"Martis RJ, Chakraborty C, Ray AK (2010) An integrated ECG feature extraction scheme using PCA and wavelet transform2009. In: Annual IEEE India conference, p 422"},{"issue":"14","key":"12614_CR17","doi-asserted-by":"publisher","first-page":"11792","DOI":"10.1016\/j.eswa.2012.04.072","volume":"39","author":"RJ Martis","year":"2012","unstructured":"Martis RJ, Acharya UR, Mandana KM, Ray AK (2012) Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst Appl 39(14):11792\u201311800","journal-title":"Expert Syst Appl"},{"issue":"5","key":"12614_CR18","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.bspc.2013.01.005","volume":"8","author":"RJ Martis","year":"2013","unstructured":"Martis RJ, Acharya UR, Min LC (2013) ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Proces 8(5):437\u2013448","journal-title":"Biomed Signal Proces"},{"key":"12614_CR19","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.compbiomed.2014.02.012","volume":"48","author":"RJ Martis","year":"2014","unstructured":"Martis RJ, Acharya UR, Adeli H (2014) Current methods in electrocardiogram characterization. Comput Biol Med 48:133\u2013149","journal-title":"Comput Biol Med"},{"key":"12614_CR20","doi-asserted-by":"crossref","unstructured":"Mathews SM, Polania LF, Barner KE (2015) Leveraging a discriminative dictionary learning algorithm for single-lead ECG classification 41st Annual Northeast Biomedical Engineering Conference, pp 1\u20132","DOI":"10.1109\/NEBEC.2015.7117118"},{"key":"12614_CR21","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.bspc.2018.08.007","volume":"47","author":"V Mond\u00e9jar-Guerra","year":"2019","unstructured":"Mond\u00e9jar-Guerra V, Novo J, Rouco J, Penedo MG (2019) Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed Signal Proces 47:41\u201348","journal-title":"Biomed Signal Proces"},{"key":"12614_CR22","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.eswa.2017.09.022","volume":"92","author":"P Plawiak","year":"2018","unstructured":"Plawiak P (2018) Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 92:334\u2013349","journal-title":"Expert Syst Appl"},{"issue":"105","key":"12614_CR23","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.eswa.2018.03.038","volume":"49-64","author":"S Raj","year":"2018","unstructured":"Raj S, Ray KC (2018) Sparse representation of ECG signals for automated recognition of cardiac arrhythmias. Expert Syst Appl 49-64(105):49\u201364","journal-title":"Expert Syst Appl"},{"key":"12614_CR24","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.cmpb.2016.08.016","volume":"136","author":"S Raj","year":"2016","unstructured":"Raj S, Ray KC, Shankar O (2016) Cardiac arrhythmia beat classification using DOST and PSO tuned SVM. Comput Meth Prog Bio 136:163\u2013177","journal-title":"Comput Meth Prog Bio"},{"key":"12614_CR25","doi-asserted-by":"publisher","first-page":"103866","DOI":"10.1016\/j.compbiomed.2020.103866","volume":"123","author":"TF Romdhane","year":"2020","unstructured":"Romdhane TF, Alhichri H, Ouni R, Atri M (2020) Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss - ScienceDirect. Comput Biol Med 123:103866","journal-title":"Comput Biol Med"},{"issue":"6","key":"12614_CR26","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/JPROC.2010.2040551","volume":"98","author":"R Rubinstein","year":"2010","unstructured":"Rubinstein R, Bruckstein AM, Elad M (2010) Dictionaries for sparse representation modeling. P IEEE 98(6):1045\u20131057","journal-title":"P IEEE"},{"issue":"3","key":"12614_CR27","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.dsp.2005.12.003","volume":"16","author":"BN Singh","year":"2006","unstructured":"Singh BN, Tiwari AK (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process 16(3):275\u2013287","journal-title":"Digit Signal Process"},{"key":"12614_CR28","volume-title":"Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms, ANSI\/AAMI EC57:1998 standard","author":"AE Standard","year":"1998","unstructured":"Standard AE (1998) Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms, ANSI\/AAMI EC57:1998 standard. Association for the Advancement of Medical Instrumentation"},{"key":"12614_CR29","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.fss.2016.06.001","volume":"318","author":"M Wan","year":"2017","unstructured":"Wan M, Lai Z, Yang G, Yang Z (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120\u2013131","journal-title":"Fuzzy Sets Syst"},{"issue":"15","key":"12614_CR30","doi-asserted-by":"publisher","first-page":"22109","DOI":"10.1007\/s11042-019-7454-2","volume":"78","author":"M Wan","year":"2019","unstructured":"Wan M, Lai Z, Ming Z, Yang G (2019) An improve face representation and recognition method based on graph regularized non-negative matrix factorization. Multimed Tools Appl 78(15):22109\u201322126","journal-title":"Multimed Tools Appl"},{"key":"12614_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2021.02.006","volume":"563","author":"M Wan","year":"2021","unstructured":"Wan M, Chen X, Zhan T, Xu C (2021) Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction. Inf Sci 563:1\u201315","journal-title":"Inf Sci"},{"issue":"2","key":"12614_CR32","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright J, Yang AY, Ganesh A, Sastry SS (2009) Robust face recognition via sparse representation. IEEE T Pattern Anal 31(2):210\u2013227","journal-title":"IEEE T Pattern Anal"},{"key":"12614_CR33","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.patcog.2018.12.023","volume":"88","author":"J Xu","year":"2019","unstructured":"Xu J, An WP, Zhang L, Zhang D (2019) Sparse, collaborative, or nonnegative representation: which helps pattern classification? Pattern Recogn 88:679\u2013688","journal-title":"Pattern Recogn"},{"key":"12614_CR34","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.patrec.2020.04.022","volume":"135","author":"JX Xu","year":"2020","unstructured":"Xu JX, Yang GP, Wang KK, Huang YW (2020) Structural sparse representation with class-specific dictionary for ECG biometric recognition. Pattern Recogn Lett 135:44\u201349","journal-title":"Pattern Recogn Lett"},{"key":"12614_CR35","doi-asserted-by":"crossref","unstructured":"Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition. IEEE International Conference on Image Processing 2010:1601\u20131604","DOI":"10.1109\/ICIP.2010.5652363"},{"issue":"3","key":"12614_CR36","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s11263-014-0722-8","volume":"109","author":"M Yang","year":"2014","unstructured":"Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vis 109(3):209\u2013232","journal-title":"Int J Comput Vis"},{"key":"12614_CR37","first-page":"471","volume-title":"International Conference on Computer Vision","author":"L Zhang","year":"2012","unstructured":"Zhang L, Yang M, Feng XC (2012) Sparse representation or collaborative representation: which helps face recognition? In: International Conference on Computer Vision, pp 471\u2013478"},{"key":"12614_CR38","doi-asserted-by":"crossref","unstructured":"Zhao W, Hu J, Jia D (2019) Deep learning based patient-specific classification of arrhythmia on ECG signal in 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 1500\u20131503","DOI":"10.1109\/EMBC.2019.8856650"},{"issue":"11","key":"12614_CR39","doi-asserted-by":"publisher","first-page":"2609","DOI":"10.3390\/s19112609","volume":"19","author":"JH Zhou","year":"2019","unstructured":"Zhou JH, Zhang B (2019) Collaborative representation using non-negative samples for image classification. Sensors-Basel 19(11):2609","journal-title":"Sensors-Basel"},{"issue":"1","key":"12614_CR40","first-page":"131","volume":"16","author":"W Zhu","year":"2019","unstructured":"Zhu W, Chen X, Wang Y, Wang L (2019) Arrhythmia recognition and classification using ECG morphology and segment feature analysis. IEEE Acm T Comput Bi 16(1):131\u2013138","journal-title":"IEEE Acm T Comput Bi"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12614-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12614-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12614-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T11:28:33Z","timestamp":1651490913000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12614-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,2]]},"references-count":40,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["12614"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12614-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,3,2]]},"assertion":[{"value":"20 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2022","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 declare that there are no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}