{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T13:54:03Z","timestamp":1770990843322,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2015,5,31]],"date-time":"2015-05-31T00:00:00Z","timestamp":1433030400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"published-print":{"date-parts":[[2015,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized<jats:italic>N<\/jats:italic>dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and<jats:italic>t<\/jats:italic>-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.<\/jats:p>","DOI":"10.1186\/s13634-015-0231-0","type":"journal-article","created":{"date-parts":[[2015,5,30]],"date-time":"2015-05-30T18:35:18Z","timestamp":1433010918000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Fast multi-scale feature fusion for ECG heartbeat classification"],"prefix":"10.1186","volume":"2015","author":[{"given":"Danni","family":"Ai","sequence":"first","affiliation":[]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zeyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jingfan","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Changbin","family":"Ai","sequence":"additional","affiliation":[]},{"given":"Yongtian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,5,31]]},"reference":[{"key":"231_CR1","unstructured":"S Mendis, P Puska, B Norrving, Global Atlas on cardiovascular disease prevention and control. World Health Organization, ISBN: 978 92 4 156437 3, 1-168 (2011)."},{"issue":"10","key":"231_CR2","doi-asserted-by":"publisher","first-page":"e24386","DOI":"10.1371\/journal.pone.0024386","volume":"6","author":"M Javadi","year":"2011","unstructured":"M Javadi, R Ebrahimpour, A Sajedin, S Faridi, S Zakernejad, Improving ECG classification accuracy using an ensemble of neural network modules. PLoS one 6(10), e24386 (2011). doi:10.1371\/journal.pone.0024386","journal-title":"PLoS one"},{"issue":"3","key":"231_CR3","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/TBME.2010.2068048","volume":"58","author":"M Llamedo","year":"2011","unstructured":"M Llamedo, JP Martinez, Heartbeat classifier using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 58(3), 616\u2013625 (2011)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"231_CR4","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.medengphy.2007.02.003","volume":"30","author":"I Jekova","year":"2008","unstructured":"I Jekova, G Bortolan, I Christov, Assessment and comparison of different methods for heartbeat classification. Med. Eng. Phys. 30(2), 248\u2013257 (2008)","journal-title":"Med. Eng. Phys."},{"key":"231_CR5","unstructured":"LI Kuncheva, Combining pattern classifiers: methods and algorithms. John Wiley & Sons, ISBN: 978-0-471-21078-8, 1-376 (2004)."},{"issue":"3","key":"231_CR6","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.cmpb.2011.10.002","volume":"105","author":"Y Kutlu","year":"2012","unstructured":"Y Kutlu, D Kuntalp, Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput. Methods Programs Biomed. 105(3), 257\u2013267 (2012)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"8","key":"231_CR7","doi-asserted-by":"publisher","first-page":"2168","DOI":"10.1109\/TBME.2011.2113395","volume":"58","author":"T Mar","year":"2011","unstructured":"T Mar, S Zaunseder, JP Martinez, M Llamedo, R Poll, Optimization of ECG classification by means of feature selection. IEEE Trans. Biomed. Eng. 58(8), 2168\u20132177 (2011)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"231_CR8","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.bspc.2013.01.005","volume":"8","author":"RJ Martis","year":"2013","unstructured":"RJ Martis, UR Acharya, CM Lim, ECG beat classification using PCA, LDA ICA and discrete wavelet transform. Biomed. Signal Process. Control 8(5), 437\u2013448 (2013)","journal-title":"Biomed. Signal Process. Control"},{"key":"231_CR9","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.neucom.2012.09.020","volume":"103","author":"DN Ai","year":"2013","unstructured":"DN Ai, GF Duan, XH Han, YW Chen, Generalized N-dimensional independent component analysis and its application to multiple feature selection and fusion for image classification. Neurocomputing 103, 186\u2013197 (2013)","journal-title":"Neurocomputing"},{"issue":"7","key":"231_CR10","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","volume":"51","author":"P de Chazal","year":"2004","unstructured":"P de Chazal, M O'Dwyer, RB Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196\u20131206 (2004)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"231_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.4236\/ijcns.2010.33041","volume":"3","author":"MY Gokhale","year":"2010","unstructured":"MY Gokhale, DK Khanduja, Time domain signal analysis using wavelet packet decomposition approach. Int. J. Commun. Network Syst. Sci. 3(3), 321\u2013329 (2010)","journal-title":"Int. J. Commun. Network Syst. Sci."},{"key":"231_CR12","doi-asserted-by":"crossref","unstructured":"I Khalil, F Sufi, Real-time ECG data transmission with wavelet packet decomposition over wireless networks, International Conference on Intelligent Sensors. Sens. Networks Inf. Process. , E-ISBN 978-1-4244-2957-8, 267\u2013272 (2008)","DOI":"10.1109\/ISSNIP.2008.4761998"},{"issue":"3","key":"231_CR13","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"TG Kolda, BW Bader, Tensor decompositions and applications. SIAM Rev. 51(3), 455\u2013500 (2009)","journal-title":"SIAM Rev."},{"issue":"4","key":"231_CR14","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1137\/S0895479898346995","volume":"21","author":"L de Lathauwer","year":"2000","unstructured":"L de Lathauwer, B de Moor, J Vandewalle, On the best rank-1 and rank-(R1, R2, \u2026, Rn) approximation of higher order tensors. SIAM J. Matrix Anal. Appl. 21(4), 1324\u20131342 (2000)","journal-title":"SIAM J. Matrix Anal. Appl."},{"issue":"1","key":"231_CR15","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1109\/TIP.2006.884929","volume":"16","author":"S Yan","year":"2007","unstructured":"S Yan, D Xu, Q Yang, H Zhang et al., Multilinear discriminant analysis for face recognition. IEEE Trans. Image Process. 16(1), 212\u2013220 (2007)","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"231_CR16","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.1016\/j.neucom.2009.01.007","volume":"72","author":"R Xu","year":"2009","unstructured":"R Xu, YW Chen, Generalized N-dimensional principal component analysis (GND-PCA) and its application on construction of statistical appearance models for medical volumes with fewer samples. Neurocomputing 72(10), 2276\u20132287 (2009)","journal-title":"Neurocomputing"},{"issue":"3","key":"231_CR17","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1109\/TIP.2011.2168417","volume":"21","author":"XH Han","year":"2012","unstructured":"XH Han, YW Chen, X Ruan, Multilinear supervised neighborhood embedding of a local descriptor tensor for scene\/object recognition. IEEE Trans. Image Process. 21(3), 1314\u20131326 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"231_CR18","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","volume":"7","author":"AJ Bell","year":"1995","unstructured":"AJ Bell, TJ Sejnowski, An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7(6), 1129\u20131159 (1995)","journal-title":"Neural Comput."},{"issue":"1","key":"231_CR19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.artmed.2008.04.007","volume":"44","author":"BM Asl","year":"2008","unstructured":"BM Asl, SK Setarehdan, M Mohebbi, Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif. Intell. Med. 44(1), 51\u201364 (2008)","journal-title":"Artif. Intell. Med."},{"key":"231_CR20","first-page":"1889","volume":"6","author":"RE Fan","year":"2005","unstructured":"RE Fan, PH Chen, CJ Lin, Working set selection using second order information for training SVM. J. Mach. Learn. Res. 6, 1889\u20131918 (2005)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"231_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2011","unstructured":"GB Moody, RG Mark, The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45\u201350 (2011)","journal-title":"IEEE Eng. Med. Biol. Mag."},{"issue":"23","key":"231_CR22","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"AL Goldberger, LAN Amaral, L Glass, JM Hausdorff, PC Ivanov, RG Mark, JE Mietus, GB Moody, CK Peng, HE Stanley, Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","journal-title":"Circulation"},{"key":"231_CR23","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.bspc.2014.01.011","volume":"10","author":"B Mali","year":"2014","unstructured":"B Mali, S Zulj, R Magjarevic, D Miklavcic, T Jarm, Matlab-based tool for ECG and HRV analysis. Biomed. Signal Process. Control 10, 108\u2013116 (2014)","journal-title":"Biomed. Signal Process. Control"},{"key":"231_CR24","doi-asserted-by":"crossref","unstructured":"RJ Martis, UR Acharya, AK Ray, C Chakraborty, Application of higher order cumulants to ECG signals for the cardiac health diagnosis, Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, August 30 2011-September 3 2011, 1697\u20131700 (2011)","DOI":"10.1109\/IEMBS.2011.6090487"},{"key":"231_CR25","volume-title":"Pattern classification","author":"RO Duda","year":"2001","unstructured":"RO Duda, PE Hart, DG Stork, Pattern classification, 2nd edn. (John Wiley & Sons, New York, 2001)","edition":"2"},{"key":"231_CR26","doi-asserted-by":"crossref","unstructured":"MK Das, S Ari, ECG beats classification using mixture of features. Int. Scholarly Res. Not. 2014, Article ID 178436, 12 pages (2014)","DOI":"10.1155\/2014\/178436"},{"issue":"12","key":"231_CR27","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1109\/TBME.2006.880879","volume":"53","author":"OT Inan","year":"2006","unstructured":"OT Inan, L Giovangrandi, GTA Kovacs, Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans. Biomed. Eng. 53(12), 2507\u20132515 (2006)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"231_CR28","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1109\/TNN.2007.900239","volume":"18","author":"W Jiang","year":"2007","unstructured":"W Jiang, SG Kong, Block-based neural networks for personalized ECG signal classification. IEEE Trans. Neural Networks 18(6), 1750\u20131761 (2007)","journal-title":"IEEE Trans. Neural Networks"},{"issue":"5","key":"231_CR29","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1109\/TBME.2009.2013934","volume":"56","author":"T Ince","year":"2009","unstructured":"T Ince, S Kiranyaz, M Gabbouj, A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans. Biomed. Eng. 56(5), 1415\u20131426 (2009)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"231_CR30","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.bspc.2012.08.004","volume":"8","author":"RJ Martis","year":"2013","unstructured":"RJ Martis, UR Acharya, KM Mandana, AK Ray, C Chakraborty, Cardiac decision making using higher order spectra. Biomed. Signal Process. Control 8(2), 193\u2013203 (2013)","journal-title":"Biomed. Signal Process. Control"}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-015-0231-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13634-015-0231-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-015-0231-0","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-015-0231-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T05:13:58Z","timestamp":1748409238000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-015-0231-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,5,31]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,12]]}},"alternative-id":["231"],"URL":"https:\/\/doi.org\/10.1186\/s13634-015-0231-0","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,5,31]]},"assertion":[{"value":"9 February 2015","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2015","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2015","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"46"}}