{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T18:31:54Z","timestamp":1773685914630,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"31","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s00521-021-06693-1","type":"journal-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T03:02:20Z","timestamp":1636772540000},"page":"22823-22837","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Hybrid feature fusion for classification optimization of short ECG segment in IoT based intelligent healthcare system"],"prefix":"10.1007","volume":"35","author":[{"given":"Xianbin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhe","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7594-1487","authenticated-orcid":false,"given":"Wanqing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor Hugo C.","family":"de Albuquerque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"6693_CR1","unstructured":"Benjamin EJ et al. (2018) Heart disease and stroke statistics: 2018 update: a report from the American Heart Association. Circulation"},{"issue":"9","key":"6693_CR2","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1056\/NEJMoa1007432","volume":"364","author":"SJ Connolly","year":"2011","unstructured":"Connolly SJ, Eikelboom J, Joyner C et al (2011) Apixaban in patients with atrial fibrillation. N Engl J Med 364(9):806\u2013817","journal-title":"N Engl J Med"},{"key":"6693_CR3","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","volume":"102","author":"\u00d6 Y\u0131ld\u0131r\u0131m","year":"2018","unstructured":"Y\u0131ld\u0131r\u0131m \u00d6 et al (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411\u2013420","journal-title":"Comput Biol Med"},{"key":"6693_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3000440","author":"W Ding","year":"2020","unstructured":"Ding W et al (2020) Smart supervision of cardiomyopathy based on fuzzy Harris Hawks optimizer and wearable sensing data optimization: a new model. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2020.3000440","journal-title":"IEEE Trans Cybern"},{"key":"6693_CR5","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.inffus.2019.06.004","volume":"53","author":"MAG Santos","year":"2020","unstructured":"Santos MAG, Munoz R, Olivares R et al (2020) Online heart monitoring systems on the internet of health things environments: a survey, a reference model and an outlook[J]. Inf Fusion 53:222\u2013239","journal-title":"Inf Fusion"},{"key":"6693_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3037759","author":"JAL Marques","year":"2020","unstructured":"Marques JAL et al (2020) IoT-based smart health system for ambulatory maternal and fetal monitoring. IEEE Internet of Things J. https:\/\/doi.org\/10.1109\/JIOT.2020.3037759","journal-title":"IEEE Internet of Things J"},{"key":"6693_CR7","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.eswa.2018.07.030","volume":"114","author":"Z Golrizkhatami","year":"2018","unstructured":"Golrizkhatami Z, Acan A (2018) ECG classification using three-level fusion of different feature descriptors. Expert Syst Appl 114:54\u201364","journal-title":"Expert Syst Appl"},{"key":"6693_CR8","doi-asserted-by":"publisher","first-page":"92871","DOI":"10.1109\/ACCESS.2019.2928017","volume":"7","author":"J Huang","year":"2019","unstructured":"Huang J et al (2019) ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access 7:92871\u201392880","journal-title":"IEEE Access"},{"key":"6693_CR9","doi-asserted-by":"publisher","first-page":"102262","DOI":"10.1016\/j.bspc.2020.102262","volume":"64","author":"BM Mathunjwa","year":"2021","unstructured":"Mathunjwa BM et al (2021) ECG arrhythmia classification by using a recurrence plot and convolutional neural network. Biomed Sig Process Control 64:102262","journal-title":"Biomed Sig Process Control"},{"key":"6693_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2021.04.004","author":"J Rahul","year":"2021","unstructured":"Rahul J et al (2021) An improved cardiac arrhythmia classification using an RR interval-based approach. Biocybern Biomed Eng. https:\/\/doi.org\/10.1016\/j.bbe.2021.04.004","journal-title":"Biocybern Biomed Eng"},{"issue":"4","key":"6693_CR11","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1109\/TBME.2010.2096506","volume":"58","author":"C Huang","year":"2010","unstructured":"Huang C et al (2010) A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Trans Biomed Eng 58(4):1113\u20131119","journal-title":"IEEE Trans Biomed Eng"},{"key":"6693_CR12","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.eswa.2017.09.022","volume":"92","author":"P P\u0142awiak","year":"2018","unstructured":"P\u0142awiak 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"},{"key":"6693_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3047962","author":"J Yang","year":"2020","unstructured":"Yang J, Yan R (2020) A multidimensional feature extraction and selection method for ECG arrhythmias classification. IEEE Sensors J. https:\/\/doi.org\/10.1109\/JSEN.2020.3047962","journal-title":"IEEE Sensors J"},{"issue":"6","key":"6693_CR14","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 (1997) Detection of life-threatening cardiac arrhythmias using the wavelet transformation. Med Biol Eng Comput 35(6):626\u2013632","journal-title":"Med Biol Eng Comput"},{"issue":"5","key":"6693_CR15","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.bspc.2011.11.003","volume":"7","author":"KMd Ashfanoor","year":"2012","unstructured":"Ashfanoor KMd, Shahnaz C (2012) Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Process Control 7(5):481\u2013489","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"6693_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0022-0736(92)90123-H","volume":"25","author":"J Slocum","year":"1992","unstructured":"Slocum J, Sahakian A, Swiryn S (1992) Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity. J Electrocardiol 25(1):1\u20138","journal-title":"J Electrocardiol"},{"key":"6693_CR17","doi-asserted-by":"publisher","first-page":"102500","DOI":"10.1016\/j.bspc.2021.102500","volume":"66","author":"N Ortigosa","year":"2021","unstructured":"Ortigosa N, Ayala G, Cano \u00d3 (2021) Variation of P-wave indices in paroxysmal atrial fibrillation patients before and after catheter ablation. Biomed Sig Process Control 66:102500","journal-title":"Biomed Sig Process Control"},{"key":"6693_CR18","doi-asserted-by":"publisher","first-page":"101875","DOI":"10.1016\/j.bspc.2020.101875","volume":"59","author":"CK Jha","year":"2020","unstructured":"Jha CK, Kolekar MH (2020) Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier. Biomed Sig Process Control 59:101875","journal-title":"Biomed Sig Process Control"},{"key":"6693_CR19","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.bspc.2017.11.010","volume":"41","author":"W Lu","year":"2018","unstructured":"Lu W, Hou H, Chu J (2018) Feature fusion for imbalanced ECG data analysis. Biomed Signal Process Control 41:152\u2013160","journal-title":"Biomed Signal Process Control"},{"issue":"10","key":"6693_CR20","doi-asserted-by":"publisher","first-page":"2721","DOI":"10.1109\/TBME.2020.2969719","volume":"67","author":"L Smital","year":"2020","unstructured":"Smital L et al (2020) Real-time quality assessment of long-term ECG signals recorded by wearables in free-living conditions. IEEE Trans Biomed Eng 67(10):2721\u20132734","journal-title":"IEEE Trans Biomed Eng"},{"key":"6693_CR21","doi-asserted-by":"crossref","unstructured":"Clifford GD et al. (2017) AF classification from a short single lead ECG recording: the PhysioNet\/computing in cardiology challenge 2017. In: 2017 Computing in cardiology (CinC). IEEE","DOI":"10.22489\/CinC.2017.065-469"},{"issue":"12","key":"6693_CR22","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1109\/TASLP.2016.2602884","volume":"24","author":"Yanmin Qian","year":"2016","unstructured":"Qian Yanmin et al (2016) Very deep convolutional neural networks for noise robust speech recognition. IEEE\/ACM Trans Audio Speech Lang Process 24(12):2263\u20132276","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6693_CR23","doi-asserted-by":"crossref","unstructured":"Tran PH et al. (2020) Wearable sensor data based human activity recognition using deep learning: a new approach. Dev Artif Intell Technol Comput Robot. 581\u2013588","DOI":"10.1142\/9789811223334_0070"},{"key":"6693_CR24","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 et al (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389\u2013396","journal-title":"Comput Biol Med"},{"key":"6693_CR25","doi-asserted-by":"publisher","first-page":"102672","DOI":"10.1016\/j.bspc.2021.102672","volume":"68","author":"QH Nguyen","year":"2021","unstructured":"Nguyen QH et al (2021) Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings. Biomed Sig Process Control 68:102672","journal-title":"Biomed Sig Process Control"},{"issue":"10","key":"6693_CR26","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.3390\/rs12101685","volume":"12","author":"A Ullah","year":"2020","unstructured":"Ullah A et al (2020) Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation. Remote Sens 12(10):1685","journal-title":"Remote Sens"},{"key":"6693_CR27","doi-asserted-by":"publisher","first-page":"89152","DOI":"10.1109\/ACCESS.2019.2926749","volume":"7","author":"X-C Cao","year":"2019","unstructured":"Cao X-C, Yao B, Chen B-Q (2019) Atrial fibrillation detection using an improved multi-Scale decomposition enhanced residual convolutional neural network. IEEE Access 7:89152\u201389161","journal-title":"IEEE Access"},{"issue":"2","key":"6693_CR28","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/JBHI.2019.2911367","volume":"24","author":"S Saadatnejad","year":"2019","unstructured":"Saadatnejad S, Oveisi M, Hashemi M (2019) LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE J Biomed Health Inform 24(2):515\u2013523","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"6693_CR29","doi-asserted-by":"publisher","first-page":"054009","DOI":"10.1088\/1361-6579\/ab15a2","volume":"40","author":"S Hong","year":"2019","unstructured":"Hong S et al (2019) Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings. Physiol Measure 40(5):054009","journal-title":"Physiol Measure"},{"issue":"7","key":"6693_CR30","doi-asserted-by":"publisher","first-page":"074002","DOI":"10.1088\/1361-6579\/aacc48","volume":"39","author":"C Liu","year":"2018","unstructured":"Liu C et al (2018) A comparison of entropy approaches for AF discrimination. Physiol Measure 39(7):074002","journal-title":"Physiol Measure"},{"key":"6693_CR31","doi-asserted-by":"crossref","unstructured":"Teolis A, John JB (1998) Computational signal processing with wavelets. Boston, MA, USA: Birkh\u00e4user","DOI":"10.1007\/978-1-4612-4142-3"},{"issue":"1s","key":"6693_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3357525","volume":"16","author":"H Zhang","year":"2020","unstructured":"Zhang H et al (2020) Active balancing mechanism for imbalanced medical data in deep learning-based classification models. ACM Trans Multimed Comput Commun Appl 16(1s):1\u201315","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"6693_CR33","unstructured":"Prati RC, Gustavo EAPAB, Maria CM (2008) A study with class imbalance and random sampling for a decision tree learning system. In: IFIP international conference on artificial intelligence in theory and practice. Springer, Boston"},{"issue":"11","key":"6693_CR34","doi-asserted-by":"publisher","first-page":"114002","DOI":"10.1088\/1361-6579\/aad386","volume":"39","author":"PA Warrick","year":"2018","unstructured":"Warrick PA, Homsi MN (2018) Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection. Physiol Measur 39(11):114002","journal-title":"Physiol Measur"},{"issue":"11","key":"6693_CR35","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Yann LeCun","year":"1998","unstructured":"LeCun Yann et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"4","key":"6693_CR36","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.patcog.2011.09.021","volume":"45","author":"X-X Niu","year":"2012","unstructured":"Niu X-X, Suen CY (2012) A novel hybrid CNN\u2013SVM classifier for recognizing handwritten digits. Pattern Recogn 45(4):1318\u20131325","journal-title":"Pattern Recogn"},{"issue":"2","key":"6693_CR37","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/JSAC.2020.3020598","volume":"39","author":"A Dourado","year":"2020","unstructured":"Dourado A, Carlos MJM et al (2020) An open IoHT-based deep learning framework for online medical image recognition. IEEE J Select Areas Commun 39(2):541\u2013548","journal-title":"IEEE J Select Areas Commun"},{"key":"6693_CR38","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2016.11.023","volume":"225","author":"P Tang","year":"2017","unstructured":"Tang P, Wang H, Kwong S (2017) G-MS2F: googLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing 225:188\u2013197","journal-title":"Neurocomputing"},{"issue":"4","key":"6693_CR39","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433\u2013459","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"key":"6693_CR40","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/TBME.1985.325532","volume":"3","author":"J Pan","year":"1985","unstructured":"Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 3:230\u2013236","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"6693_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2009.11.001","volume":"5","author":"R Alcaraz","year":"2010","unstructured":"Alcaraz R, Rieta JJ (2010) A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms. Biomed Sig Process Control 5(1):1\u201314","journal-title":"Biomed Sig Process Control"},{"issue":"2","key":"6693_CR42","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.cmpb.2009.08.010","volume":"98","author":"P Mic\u00f3","year":"2010","unstructured":"Mic\u00f3 P et al (2010) Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy. Comput Methods Prog Biomed 98(2):118\u2013129","journal-title":"Comput Methods Prog Biomed"},{"key":"6693_CR43","doi-asserted-by":"crossref","unstructured":"Xiong Z, Stiles MK, Zhao J (2017) Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. In: 2017 computing in cardiology (CinC). IEEE","DOI":"10.22489\/CinC.2017.066-138"},{"issue":"3","key":"6693_CR44","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.cmpb.2014.09.002","volume":"117","author":"Q Li","year":"2014","unstructured":"Li Q, Rajagopalan C, Clifford GD (2014) A machine learning approach to multi-level ECG signal quality classification. Comput Methods Prog Biomed 117(3):435\u2013447","journal-title":"Comput Methods Prog Biomed"},{"key":"6693_CR45","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.measurement.2018.05.033","volume":"125","author":"M Hammad","year":"2018","unstructured":"Hammad M et al (2018) Detection of abnormal heart conditions based on characteristics of ECG signals. Measurement 125:634\u2013644","journal-title":"Measurement"},{"key":"6693_CR46","doi-asserted-by":"publisher","first-page":"107567","DOI":"10.1016\/j.patcog.2020.107567","volume":"109","author":"T Hussain","year":"2021","unstructured":"Hussain T et al (2021) A comprehensive survey of multi-view video summarization. Pattern Recognit 109:107567","journal-title":"Pattern Recognit"},{"key":"6693_CR47","doi-asserted-by":"publisher","first-page":"101675","DOI":"10.1016\/j.bspc.2019.101675","volume":"5","author":"P Cao","year":"2020","unstructured":"Cao P et al (2020) A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation. Biomed Sig Process Control 5:101675","journal-title":"Biomed Sig Process Control"},{"issue":"9","key":"6693_CR48","doi-asserted-by":"publisher","first-page":"2461","DOI":"10.1109\/JBHI.2020.2981526","volume":"24","author":"R Wang","year":"2020","unstructured":"Wang R, Fan J, Li Y (2020) Deep multi-scale fusion neural network for multi-class arrhythmia detection. IEEE J Biomed Health Inform 24(9):2461\u20132472","journal-title":"IEEE J Biomed Health Inform"},{"key":"6693_CR49","doi-asserted-by":"crossref","unstructured":"Fayyazifar N (2021) An accurate CNN architecture for atrial fibrillation detection using neural architecture search. In: 2020 28th European signal processing conference (EUSIPCO) (pp. 1135\u20131139). IEEE","DOI":"10.23919\/Eusipco47968.2020.9287496"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06693-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06693-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06693-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T18:02:51Z","timestamp":1697565771000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06693-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,13]]},"references-count":49,"journal-issue":{"issue":"31","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["6693"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06693-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,13]]},"assertion":[{"value":"12 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2021","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 state that they have no known competing financial interests or personal relationships that could affect the work reported in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}