{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:38:24Z","timestamp":1742913504038,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030966379"},{"type":"electronic","value":"9783030966386"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-96638-6_34","type":"book-chapter","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T03:03:09Z","timestamp":1646967789000},"page":"322-331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Web-Based Software Tool for Electrocardiogram Annotation"],"prefix":"10.1007","author":[{"given":"Todor","family":"Stoyanov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.bspc.2017.01.009","volume":"34","author":"I Jekova","year":"2017","unstructured":"Jekova, I., et al.: A real-time quality monitoring system for optimal recording of 12-lead resting ECG. Biomed. Signal Process. Control. 34, 126\u2013133 (2017)","journal-title":"Biomed. Signal Process. Control."},{"key":"34_CR2","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1088\/0967-3334\/33\/9\/1463","volume":"33","author":"I Jekova","year":"2012","unstructured":"Jekova, I., Krasteva, V., Christov, I., Ab\u00e4cherli, R.: Threshold-based system for noise detection in multilead ECG recordings. Physiol. Meas. 33, 1463\u20131477 (2012)","journal-title":"Physiol. Meas."},{"issue":"11","key":"34_CR3","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1007\/s10439-008-9553-5","volume":"36","author":"S Tabakov","year":"2008","unstructured":"Tabakov, S., Iliev, I., Krasteva, V.: Online digital filter and QRS detector applicable in low resource ECG monitoring systems. Ann. Biomed. Eng. 36(11), 1805\u20131815 (2008)","journal-title":"Ann. Biomed. Eng."},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Christov, I.: Real time electrocardiogram QRS detection using combined adaptive threshold. BioMed. Eng. OnLine. 3, Article number 28 (2004)","DOI":"10.1186\/1475-925X-3-28"},{"key":"34_CR5","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1088\/0967-3334\/28\/3\/003","volume":"28","author":"I Iliev","year":"2007","unstructured":"Iliev, I., Krasteva, V., Tabakov, S.: Real-time detection of pathological cardiac events in the electrocardiogram. Physiol. Meas. 28, 259\u2013276 (2007)","journal-title":"Physiol. Meas."},{"issue":"10","key":"34_CR6","doi-asserted-by":"publisher","first-page":"e0140123","DOI":"10.1371\/journal.pone.0140123","volume":"10","author":"V Krasteva","year":"2015","unstructured":"Krasteva, V., Jekova, I., Leber, R., Schmid, R., Ab\u00e4cherli, R.: Superiority of classification tree versus cluster, fuzzy and discriminant models in a heartbeat classification system. PLoS One 10(10), e0140123 (2015)","journal-title":"PLoS One"},{"key":"34_CR7","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1088\/0967-3334\/23\/2\/309","volume":"23","author":"I Jekova","year":"2002","unstructured":"Jekova, I., Dushanova, J., Popivanov, D.: Method for ventricular fibrillation detection in the external electrocardiogram using nonlinear prediction. Physiol. Meas. 23, 337\u2013345 (2002)","journal-title":"Physiol. Meas."},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1088\/0967-3334\/23\/4\/303","volume":"23","author":"I Jekova","year":"2002","unstructured":"Jekova, I., Mitev, P.: Detection of ventricular fibrillation and tachycardia from the surface ECG by a set of parameters acquired from four methods. Physiol. Meas. 23, 629\u2013634 (2002)","journal-title":"Physiol. Meas."},{"key":"34_CR9","first-page":"737","volume":"42","author":"V Krasteva","year":"2015","unstructured":"Krasteva, V., Jekova, I., Leber, R., Schmid, R., Ab\u00e4cherli, R.: Validation of arrhythmia detection library on bedside monitor data for triggering alarms in intensive care. Comput. Cardiol. 42, 737\u2013740 (2015)","journal-title":"Comput. Cardiol."},{"key":"34_CR10","doi-asserted-by":"publisher","first-page":"1326","DOI":"10.1007\/s10439-009-9885-9","volume":"38","author":"V Krasteva","year":"2010","unstructured":"Krasteva, V., Jekova, I., Dotsinsky, I., Didon, J.P.: Shock advisory system for heart rhythm analysis during cardiopulmonary resuscitation using a single ECG input of automated external defibrillators. Ann. Biomed. Eng. 38, 1326\u20131336 (2010)","journal-title":"Ann. Biomed. Eng."},{"key":"34_CR11","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1088\/0967-3334\/37\/8\/1273","volume":"37","author":"V Krasteva","year":"2016","unstructured":"Krasteva, V., Jekova, I., Leber, R., Schmid, R., Ab\u00e4cherli, R.: Real-time arrhythmia detection with supplementary ECG quality and pulse wave monitoring for reduction of false alarms in ICU. Physiol. Meas. 37, 1273\u20131297 (2016)","journal-title":"Physiol. Meas."},{"issue":"6","key":"34_CR12","doi-asserted-by":"publisher","first-page":"094005","DOI":"10.1088\/1361-6579\/aad9f0","volume":"39","author":"I Christov","year":"2018","unstructured":"Christov, I., Krasteva, V., Simova, I., Neycheva, T., Schmid, R.: Ranking of the most reliable beat morphology and heart rate variability features for the detection of atrial fibrillation in short single-lead ECG. Physiological Measurement. 39(6), 094005 (2018)","journal-title":"Physiological Measurement."},{"issue":"2","key":"34_CR13","doi-asserted-by":"publisher","first-page":"153","DOI":"10.7546\/ijba.2020.24.2.000743","volume":"24","author":"I Jekova","year":"2020","unstructured":"Jekova, I., Bortolan, G., Stoyanov, T., Dotsinsky, I.: Multi-type arrhythmia classification: assessment of the potential of time and frequency domain features and different classifiers. Int. J. Bioautom. 24(2), 153\u2013172 (2020)","journal-title":"Int. J. Bioautom."},{"issue":"1","key":"34_CR14","first-page":"193","volume":"7","author":"M Matveev","year":"2007","unstructured":"Matveev, M., Krasteva, V., Naydenov, S., Donova, T.: Possibilities of signal-averaged orthogonal and vector electrocardiography for locating and size evaluation of acute myocardial infarction with ST-elevation. Anatol. J. Cardiol. 7(1), 193\u2013197 (2007)","journal-title":"Anatol. J. Cardiol."},{"key":"34_CR15","first-page":"461","volume":"33","author":"M Matveev","year":"2006","unstructured":"Matveev, M., Naydenov, S., Krasteva, V., Donova, T., Christov, I.: Assessment of the infarct size in high-resolution electrocardiograms. Comput. Cardiol. 33, 461\u2013464 (2006)","journal-title":"Comput. Cardiol."},{"key":"34_CR16","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1088\/0967-3334\/25\/5\/008","volume":"25","author":"I Jekova","year":"2004","unstructured":"Jekova, I., Mougeolle, F., Valance, A.: Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram. Physiol. Meas. 25, 1179\u20131188 (2004)","journal-title":"Physiol. Meas."},{"issue":"21","key":"34_CR17","doi-asserted-by":"publisher","first-page":"7505","DOI":"10.3390\/app10217505","volume":"10","author":"I Jekova","year":"2020","unstructured":"Jekova, I., Iliev, I., Tabakov, S.: Application of Stockwell transform and Shannon energy for pace pulses detection in a single lead ECG corrupted by EMG artifacts. Appl. Sci. 10(21), 7505 (2020)","journal-title":"Appl. Sci."},{"issue":"21","key":"34_CR18","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.1016\/j.jacc.2017.03.571","volume":"69","author":"C Krittanawong","year":"2017","unstructured":"Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., Kitai, T.: Artificial intelligence in precision cardiovascular medicine. J. Am. Coll. Cardiol. 69(21), 2657\u20132664 (2017)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"1","key":"34_CR19","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","volume":"25","author":"A Hannun","year":"2019","unstructured":"Hannun, A., et al.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25(1), 65 (2019)","journal-title":"Nat. Med."},{"issue":"1","key":"34_CR20","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","volume":"25","author":"ZI Attia","year":"2019","unstructured":"Attia, Z.I., Kapa, S., Lopez-Jimenez, F., et al.: Screening for cardiac contractile dysfunction using an artificial intelligence\u2013enabled electrocardiogram. Nat. Med. 25(1), 70 (2019)","journal-title":"Nat. Med."},{"issue":"10","key":"34_CR21","doi-asserted-by":"publisher","first-page":"s20102875","DOI":"10.3390\/s20102875","volume":"20","author":"V Krasteva","year":"2020","unstructured":"Krasteva, V., M\u00e9n\u00e9tr\u00e9, S., Didon, J.P., Jekova, I.: Fully convolutional deep neural networks with optimized hyperparameters for detection of shockable and Non-shockable rhythms. Sensors. 20(10), s20102875 (2020)","journal-title":"Sensors."},{"key":"34_CR22","first-page":"17","volume":"40","author":"GB Moody","year":"2013","unstructured":"Moody, G.B.: Lightwave: Waveform and annotation viewing and editing in a web browser. Comput. Cardiol. 40, 17\u201320 (2013)","journal-title":"Comput. Cardiol."},{"issue":"23","key":"34_CR23","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"A Goldberger","year":"2000","unstructured":"Goldberger, A., Amaral, L., Glass, L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","journal-title":"Circulation"},{"key":"34_CR24","first-page":"605","volume":"42","author":"L Citi","year":"2015","unstructured":"Citi, L., Olariu, C., Barbieri, R.: A LightWAVE client for semi-automated annotation of heart beats from ECG time series. Comput. Cardiol. 42, 605\u2013608 (2015)","journal-title":"Comput. Cardiol."},{"issue":"5","key":"34_CR25","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/MCSE.2016.91","volume":"18","author":"RL Winslow","year":"2016","unstructured":"Winslow, R.L., Granite, S., Jurado, C.: WaveformECG: a platform for visualizing, annotating, and analyzing ECG data. Comput. Sci. Eng. 18(5), 36\u201346 (2016)","journal-title":"Comput. Sci. Eng."},{"key":"34_CR26","unstructured":"EcgEditor. https:\/\/github.com\/Unisens\/EcgEditor. Accessed 27 Oct 2020"},{"key":"34_CR27","unstructured":"ECG_Viewer. https:\/\/github.com\/jramshur\/ECG_Viewer. Accessed 27 Oct 2020"},{"key":"34_CR28","unstructured":"BSS_ECG, https:\/\/github.com\/AdnanHidic\/bss_ecg. Accessed 27 Oct 2020"},{"key":"34_CR29","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/978-3-030-33327-0_13","volume-title":"MLMECH 2019\/CVII-STENT 2019","author":"Z Ding","year":"2019","unstructured":"Ding, Z., Qiu, S., Guo, Y.: LabelECG: a web-based tool for distributed electrocardiogram annotation. In: Liao, H., et al. (eds.) MLMECH 2019\/CVII-STENT 2019. LNCS, vol. 11794, pp. 104\u2013111. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33327-0_13"},{"key":"34_CR30","unstructured":"The First China ECG Intelligent Competition. http:\/\/mdi.ids.tsinghua.edu.cn\/#\/. Accessed 27 Oct 2020"},{"key":"34_CR31","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/CIC.2005.1588060","volume":"32","author":"MB Oefinger","year":"2005","unstructured":"Oefinger, M.B., Mark, R.G.: A web-based tool for cisualization and collaborative annotation of physiological databases. Comput. Cardiol. 32, 163\u2013165 (2005)","journal-title":"Comput. Cardiol."},{"key":"34_CR32","unstructured":"VIEWECG WEB. https:\/\/www.amps-llc.com\/resting-ecgs\/viewECG%20Web\/ Accessed 18 Dec 2020"},{"key":"34_CR33","unstructured":"NOTOCORD. http:\/\/www.notocord.com\/solutions\/ecg. Accessed 18 Dec 2020"},{"key":"34_CR34","unstructured":"Anaconda. https:\/\/www.anaconda.com\/products\/individual. Accessed 18 Dec 2020"},{"key":"34_CR35","unstructured":"Django makes it easier to build better Web apps more quickly and with less code. https:\/\/www.djangoproject.com\/. Accessed 27 Oct 2020"},{"key":"34_CR36","unstructured":"Most Widely Deployed SQL Database Estimates. https:\/\/sqlite.org\/mostdeployed.html. Accessed 27 Oct 2020"},{"key":"34_CR37","unstructured":"Node Package Manager (NPM) environment. https:\/\/nodejs.org\/en\/. Accessed 18 Dec 2020"},{"key":"34_CR38","unstructured":"Angular. https:\/\/angular.io\/. Accessed 18 Dec 2020"},{"key":"34_CR39","unstructured":"Vue.js Frame works. https:\/\/vuejs.org\/. Accessed 18 Dec 2020"},{"key":"34_CR40","unstructured":"React.js. https:\/\/reactjs.org\/. Accessed 18 Dec 2020"}],"container-title":["Lecture Notes in Networks and Systems","Contemporary Methods in Bioinformatics and Biomedicine and Their Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96638-6_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T03:06:43Z","timestamp":1646968003000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96638-6_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030966379","9783030966386"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96638-6_34","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"12 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BioInfoMed","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Symposium on Bioinformatics and Biomedicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Burgas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bulgaria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bioinfomed2020a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bioinfomed.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}