{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T19:50:03Z","timestamp":1771271403834,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"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":[[2022,4]]},"DOI":"10.1007\/s11042-021-10994-x","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T17:02:54Z","timestamp":1620838974000},"page":"13319-13333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["QRS detection of ECG signal using U-Net and DBSCAN"],"prefix":"10.1007","volume":"81","author":[{"given":"Huiqian","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijia","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinzhao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaining","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gwanggil","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"issue":"2","key":"10994_CR1","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1109\/TBME.2007.912658","volume":"55","author":"NM Arzeno","year":"2008","unstructured":"Arzeno NM, Deng Z, Poon C (2008) Analysis of first-derivative based QRS detection algorithms. IEEE Trans Biomed Eng 55(2):478\u2013484. https:\/\/doi.org\/10.1109\/TBME.2007.912658","journal-title":"IEEE Trans Biomed Eng"},{"key":"10994_CR2","doi-asserted-by":"publisher","unstructured":"Benitez DS, Gaydecki PA, Zaidi A, Fitzpatrick AP (2000) A new QRS detection algorithm based on the Hilbert transform. In: Computers in Cardiology. vol 27 (Cat. 00CH37163), 24\u201327 Sept (2000 2000), pp379\u2013382. https:\/\/doi.org\/10.1109\/CIC.2000.898536","DOI":"10.1109\/CIC.2000.898536"},{"key":"10994_CR3","doi-asserted-by":"publisher","first-page":"97082","DOI":"10.1109\/access.2020.2997473","volume":"8","author":"W Cai","year":"2020","unstructured":"Cai W, Hu D (2020) QRS complex detection using novel deep learning neural networks. IEEE Access 8:97082\u201397089. https:\/\/doi.org\/10.1109\/access.2020.2997473","journal-title":"IEEE Access"},{"key":"10994_CR4","unstructured":"Ester M, Kriegel H-P, Sander J, Xu XA (1996) Density-based algorithm for discovering clusters in large spatial databases with noise. In: The 2nd International Conference on Knowledge Discovery and Data Mining, Portland, WA, pp 226\u2013231"},{"key":"10994_CR5","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.bspc.2015.09.008","volume":"24","author":"S Farashi","year":"2016","unstructured":"Farashi S (2016) A multiresolution time-dependent entropy method for QRS complex detection. Biomed Signal Process Control 24:63\u201371. https:\/\/doi.org\/10.1016\/j.bspc.2015.09.008","journal-title":"Biomed Signal Process Control"},{"issue":"23","key":"10994_CR6","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals. Circulation 101(23):E215\u2013E220. https:\/\/doi.org\/10.1161\/01.CIR.101.23.e215","journal-title":"Circulation"},{"key":"10994_CR7","doi-asserted-by":"publisher","unstructured":"Hamdi S, Ben Abdallah A, Bedoui MH (2017) Real time QRS complex detection using DFA and regular grammar. Biomed Eng Online 16:1\u201320. https:\/\/doi.org\/10.1186\/s12938-017-0322-2","DOI":"10.1186\/s12938-017-0322-2"},{"key":"10994_CR8","doi-asserted-by":"publisher","unstructured":"Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY (2019) Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 25(1):65-65+. https:\/\/doi.org\/10.1038\/s41591-018-0268-3","DOI":"10.1038\/s41591-018-0268-3"},{"key":"10994_CR9","doi-asserted-by":"publisher","first-page":"16979","DOI":"10.1109\/access.2020.2967775","volume":"8","author":"M Jia","year":"2020","unstructured":"Jia M, Li F, Wu J, Chen Z, Pu Y (2020) Robust QRS detection using high-resolution wavelet packet decomposition and time-attention convolutional neural network. IEEE Access 8:16979\u201316988. https:\/\/doi.org\/10.1109\/access.2020.2967775","journal-title":"IEEE Access"},{"issue":"3","key":"10994_CR10","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/tbme.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2016","unstructured":"Kiranyaz S, Ince T, Gabbouj M (2016) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63(3):664\u2013675. https:\/\/doi.org\/10.1109\/tbme.2015.2468589","journal-title":"IEEE Trans Biomed Eng"},{"key":"10994_CR11","doi-asserted-by":"publisher","unstructured":"Labati RD, Munoz E, Piuri V, Sassi R, Scotti F (2019) Deep-ECG: Convolutional Neural Networks for ECG biometric recognition. Pattern Recogn Lett 126:78\u201385. https:\/\/doi.org\/10.1016\/j.patrec.2018.03.028","DOI":"10.1016\/j.patrec.2018.03.028"},{"key":"10994_CR12","doi-asserted-by":"publisher","unstructured":"Lee JS, Lee SJ, Choi M, Seo M, Kim SW (2019) QRS detection method based on fully convolutional networks for capacitive electrocardiogram. Expert Syst Appl 134:66\u201378. https:\/\/doi.org\/10.1016\/j.eswa.2019.05.033","DOI":"10.1016\/j.eswa.2019.05.033"},{"key":"10994_CR13","doi-asserted-by":"publisher","unstructured":"Manikandan MS, Soman KP (2012) A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed Signal Process Control 7(2):118\u2013128. https:\/\/doi.org\/10.1016\/j.bspc.2011.03.004","DOI":"10.1016\/j.bspc.2011.03.004"},{"issue":"3","key":"10994_CR14","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.cmpb.2015.06.003","volume":"121","author":"M Merah","year":"2015","unstructured":"Merah M, Abdelmalik TA, Larbi BH (2015) R-peaks detection based on stationary wavelet transform. Comput Methods Prog Biomed 121(3):149\u2013160. https:\/\/doi.org\/10.1016\/j.cmpb.2015.06.003","journal-title":"Comput Methods Prog Biomed"},{"issue":"3","key":"10994_CR15","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 20(3):45\u201350. https:\/\/doi.org\/10.1109\/51.932724","journal-title":"IEEE Eng Med Biol Mag"},{"key":"10994_CR16","doi-asserted-by":"publisher","unstructured":"Nakai Y, Izumi S, Nakano M, Yamashita K, Fujii T, Kawaguchi H, Yoshimoto M (2014) Noise tolerant QRS detection using template matching with short-term autocorrelation. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 26\u201330 Aug. 2014 2014. pp 34\u201337. https:\/\/doi.org\/10.1109\/EMBC.2014.6943522","DOI":"10.1109\/EMBC.2014.6943522"},{"key":"10994_CR17","doi-asserted-by":"publisher","unstructured":"Nayak C, Saha SK, Kar R, Mandal D (2019) An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal. Biomed Signal Process Control 49:440\u2013464. https:\/\/doi.org\/10.1016\/j.bspc.2018.09.005","DOI":"10.1016\/j.bspc.2018.09.005"},{"key":"10994_CR18","doi-asserted-by":"publisher","unstructured":"Oh SL, Ng EYK, Tan RS, 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. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.06.002","DOI":"10.1016\/j.compbiomed.2018.06.002"},{"issue":"3","key":"10994_CR19","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/TBME.1985.325532","volume":"32","author":"J Pan","year":"1985","unstructured":"Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng BME 32(3):230\u2013236. https:\/\/doi.org\/10.1109\/TBME.1985.325532","journal-title":"IEEE Trans Biomed Eng BME"},{"key":"10994_CR20","doi-asserted-by":"publisher","unstructured":"Peimankar A, Puthusserypady S (2021) DENS-ECG: A deep learning approach for ECG signal delineation. Expert Syst Appl 165. https:\/\/doi.org\/10.1016\/j.eswa.2020.113911","DOI":"10.1016\/j.eswa.2020.113911"},{"key":"10994_CR21","doi-asserted-by":"publisher","unstructured":"Phukpattaranont P (2015) QRS detection algorithm based on the quadratic filter. Expert Syst Appl 42(11):4867\u20134877. https:\/\/doi.org\/10.1016\/j.eswa.2015.02.012","DOI":"10.1016\/j.eswa.2015.02.012"},{"key":"10994_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/5980541","volume":"2017","author":"Q Qin","year":"2017","unstructured":"Qin Q, Li J, Yue Y, Liu C (2017) An adaptive and time-efficient ECG R-peak detection algorithm. J Healthc Eng 2017:1\u201314. https:\/\/doi.org\/10.1155\/2017\/5980541","journal-title":"J Healthc Eng"},{"issue":"3","key":"10994_CR23","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.bbe.2017.02.002","volume":"37","author":"M Rakshit","year":"2017","unstructured":"Rakshit M, Das S (2017) An efficient wavelet-based automated R-peaks detection method using Hilbert transform. Biocybern Biomed Eng 37(3):566\u2013577. https:\/\/doi.org\/10.1016\/j.bbe.2017.02.002","journal-title":"Biocybern Biomed Eng"},{"key":"10994_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P (2015) Brox TU-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. Springer International Publishing, Cham, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10994_CR25","doi-asserted-by":"crossref","unstructured":"Sarlija M, Jurisic F, Popovic S (2017) A convolutional neural network based approach to QRS detection. In: Kovacic S, Loncaric S, Kristan M, Struc V, Vucic M (eds) Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis. International Symposium on Image and Signal Processing and Analysis, pp 121\u2013125","DOI":"10.1109\/ISPA.2017.8073581"},{"key":"10994_CR26","doi-asserted-by":"publisher","unstructured":"Sharma T, Sharma KK (2017) QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Comput Biol Med 87:187\u2013199. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.05.027","DOI":"10.1016\/j.compbiomed.2017.05.027"},{"key":"10994_CR27","doi-asserted-by":"publisher","unstructured":"Wang X, Zou QQRS (2019) Detection in ECG signal based on residual network. In: 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), 12\u201315 June 2019, pp 73\u201377. https:\/\/doi.org\/10.1109\/ICCSN.2019.8905308","DOI":"10.1109\/ICCSN.2019.8905308"},{"key":"10994_CR28","doi-asserted-by":"publisher","unstructured":"Xiang Y, Lin Z, Meng J (2018) Automatic QRS complex detection using two-level convolutional neural network. Biomed Eng Online 17:1\u201317. https:\/\/doi.org\/10.1186\/s12938-018-0441-4","DOI":"10.1186\/s12938-018-0441-4"},{"key":"10994_CR29","unstructured":"Xu X, Liu Y (2004) ECG QRS complex detection using slope vector waveform (SVW) algorithm. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 2004, pp. 3597\u20133600"},{"key":"10994_CR30","doi-asserted-by":"publisher","unstructured":"Yang H, Huang M, Cai Z, Yao Y, Liu CA, Faster R (2019) CNN-based real-time QRS detector. In: 2019 Computing in cardiology (CinC), 8\u201311 Sept. 2019. pp 4. https:\/\/doi.org\/10.23919\/CinC49843.2019.9005798","DOI":"10.23919\/CinC49843.2019.9005798"},{"key":"10994_CR31","doi-asserted-by":"publisher","unstructured":"Zhou Y, Hu X, Tang Z, Ahn AC (2016) Sparse representation-based ECG signal enhancement and QRS detection. Physiol Meas 37(12):2093\u20132110. https:\/\/doi.org\/10.1088\/0967-3334\/37\/12\/2093","DOI":"10.1088\/0967-3334\/37\/12\/2093"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10994-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10994-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10994-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T08:20:36Z","timestamp":1651047636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10994-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,12]]},"references-count":31,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["10994"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10994-x","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,12]]},"assertion":[{"value":"31 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 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 declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}