Relevant bibliographies by topics / Electrocardiograms (ECGs)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Electrocardiograms (ECGs)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Electrocardiograms (ECGs)"

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ABDUL-SADA, JAFAR W., and H. J. ABBAS. "Automatic diagnosis from electrocardiograms (ECGs)." International Journal of Systems Science 19, no. 11 (1988): 2157–62. http://dx.doi.org/10.1080/00207728808964108.

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Marcolino, Milena Soriano, Daniel Moore Freitas Palhares, Maria Beatriz Moreira Alkmim, and Antonio Luiz Ribeiro. "Prevalence of normal electrocardiograms in primary care patients." Revista da Associação Médica Brasileira 60, no. 3 (2014): 236–41. http://dx.doi.org/10.1590/1806-9282.60.03.012.

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Objective: Knowing the proportion the proportion of normal and abnormal electrocardiograms (ECGs) in primary care patients allows us to estimate the proportion of exams that can be analyzed by the general practitioner with minimal training in ECG interpretation, in addition to being epidemiologically relevant. The objective of this study is to assess the prevalence of normal ECGs in primary care patients. Methods: all digital ECGs analyzed by the cardiologists of Telehealth Network of Minas Gerais (TNMG) in 2011 were evaluated. TNMG is a public telehealth service that provides support to prima
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Patwardhan, Abhijit, Sachin Moghe, Ke Wang, and Fabio Leonelli. "Frequency modulation within electrocardiograms during ventricular fibrillation." American Journal of Physiology-Heart and Circulatory Physiology 279, no. 2 (2000): H825—H835. http://dx.doi.org/10.1152/ajpheart.2000.279.2.h825.

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Periods of reentrant activation and effective refractory periods are correlated with dominant frequency or reciprocal of cycle periods during ventricular fibrillation (VF). In the present study, we used an analysis technique based on Wigner transforms to quantify time-varying dominant frequencies in electrocardiograms (ECGs) during VF. We estimated dominant frequencies within orthogonal ECGs recorded in 10 dogs during trials of 10 s of VF and in 9 dogs during trials of 30 s of VF. In four additional dogs, we compared dominant frequencies during 10 s of VF before and after administration of ami
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Mischke, K., M. Zarse, M. Perkuhn, et al. "Telephonic transmission of 12-lead electrocardiograms during acute myocardial infarction." Journal of Telemedicine and Telecare 11, no. 4 (2005): 185–90. http://dx.doi.org/10.1258/1357633054068928.

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To test the feasibility of a small and simple system for telephonic transmission of 12-lead electrocardiograms (ECGs), 70 patients with acute coronary syndrome admitted to the cardiac care unit (CCU) were included in a feasibility study. The transmission system consisted of a belt with multiple electrodes, which was positioned around the chest. The ECG signal was sent to a call centre via a standard telephone line. In parallel, a standard 12-lead ECG was recorded on site. In a retrospective analysis, each lead of the transmitted ECG was compared with the on-site 12-lead ECG with regard to ST-s
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Selker, Harry P., Manlik Kwong, Robin Ruthazer, et al. "An example of medical device-based projection of clinical trial enrollment: Use of electrocardiographic data to identify candidates for a trial in acute coronary syndromes." Journal of Clinical and Translational Science 2, no. 6 (2018): 377–83. http://dx.doi.org/10.1017/cts.2019.365.

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AbstractBackground:To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.Methods:To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the plan
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Soumiaa, Mhamed-Amine, Sara Elhabbari, and Mohamed Mansouri. "The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases." MENDEL 28, no. 2 (2022): 62–66. http://dx.doi.org/10.13164/mendel.2022.2.062.

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Cardiovascular diseases (CVD) continues to be the leading cause of death worldwide, with over 17 million deaths each year. In 2015, approximately 422 million people suffered from cardiovascular disease (CVD). Reading and analyzing electrocardiograms (ECGs) can be time consuming, and the development of decision support tools based on automated systems can facilitate and speed up the diagnosis of ECGs. In this paper, we propose a 12 leads ECG signals classification using Multi-level Discrete Wavelet Transform and ResNet34 Deep Learning algorithm which classifies 8 types of cardiovascular disease
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Ross, Rebecca, Hilton Jones, and Mark Osinski. "Assessment of Analyst Consistency in Marking Electrocardiograms (ECGs)." Journal of Pharmacological and Toxicological Methods 88 (November 2017): 224. http://dx.doi.org/10.1016/j.vascn.2017.09.183.

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Wang, Lei, Shipeng Dang, Shuangxiong Chen, Jin-Yu Sun, Ru-Xing Wang, and Feng Pan. "Deep-Learning-Based Detection of Paroxysmal Supraventricular Tachycardia Using Sinus-Rhythm Electrocardiograms." Journal of Clinical Medicine 11, no. 15 (2022): 4578. http://dx.doi.org/10.3390/jcm11154578.

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Background: Paroxysmal supraventricular tachycardia (PSVT) is a common arrhythmia associated with palpitation and a decline in quality of life. However, it is undetectable with sinus-rhythmic ECGs when patients are not in the symptomatic onset stage. Methods: In the current study, a convolution neural network (CNN) was trained with normal-sinus-rhythm standard 12-lead electrocardiographs (ECGs) of negative control patients and PSVT patients to identify patients with unrecognized PSVT. PSVT refers to atrioventricular nodal reentry tachycardia or atrioventricular reentry tachycardia based on a c
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Makimoto, Hisaki, Takayuki Okatani, Masanori Suganuma, et al. "Identifying Ventricular Dysfunction Indicators in Electrocardiograms via Artificial Intelligence-Driven Analysis." Bioengineering 11, no. 11 (2024): 1069. http://dx.doi.org/10.3390/bioengineering11111069.

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Recent studies highlight artificial intelligence’s ability to identify ventricular dysfunction via electrocardiograms (ECGs); however, specific indicative waveforms remain unclear. This study analysed ECG and echocardiography data from 17,422 cases in Japan and Germany. We developed 10-layer convolutional neural networks to detect left ventricular ejection fractions below 50%, using four-fold cross-validation. Model performance, evaluated among different ECG configurations (3 s strips, single-beat, and two-beat overlay) and segments (PQRST, QRST, P, QRS, and PQRS), showed two-beat ECGs perform
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Bonaventura, Klaus, Ernst Wellnhofer, and Eckart Fleck. "Comparison of Standard and Derived 12-Lead Electrocardiograms Registrated by a Simplified 3-Lead Setting with Four Electrodes for Diagnosis of Coronary Angioplasty-induced Myocardial Ischaemia." European Cardiology Review 8, no. 3 (2012): 179. http://dx.doi.org/10.15420/ecr.2012.8.3.179.

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Electrocardiograms (ECGs), myocardial infarction, patient monitoring, EASI lead ECG, percutaneous transluminal coronary angioplasty, four electrodes set 12-lead ECG, 12-lead ECG, cardiovascular patients
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Dissertations / Theses on the topic "Electrocardiograms (ECGs)"

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Lima, Marcelo Araújo. "Sistema remoto para análise automática de ECGs nos padrões HL7 AECG e DICOM-ECG." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/26078.

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LIMA, M. A. Sistema remoto para análise automática de ECGs nos padrões HL7 AECG e DICOM-ECG. 2017. 78 f. Dissertação (Mestrado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2017.<br>Submitted by Marlene Sousa (mmarlene@ufc.br) on 2017-09-25T17:22:14Z No. of bitstreams: 1 2017_dis_malima.pdf: 1997199 bytes, checksum: 6819aa354bd4c431de68f3bd5aca3b39 (MD5)<br>Approved for entry into archive by Marlene Sousa (mmarlene@ufc.br) on 2017-09-25T17:23:17Z (GMT) No. of bitstreams: 1 2017_dis_malima.pdf: 1997199 bytes, checksum: 6819aa354bd4c431de68f3bd
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Gallo, Tyler, Joseph Beck, Daniel Clark, and Daniel Malone. "A Pilot Study to Examine the Feasibility of Measuring the QT Interval at Community Health Fairs." The University of Arizona, 2016. http://hdl.handle.net/10150/613962.

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Class of 2016 Abstract<br>Objectives: This study assessed the feasibility of using a hand-held single lead ECG device to collect readable electrocardiograms (ECGs) in a community setting among the general population. Next, the goal was to determine if QT intervals could be measured from the collected ECGs. Additionally, this study was designed to examine if patients who had a prolonged QT interval were taking medications that are associated with an increased risk of prolonged QT interval. Methods: This prospective pilot study involved collecting ECGs via an AliveCor hand-held device at commun
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Peasgood, William. "Enhancement of the abdominal fetal electrocardiogram." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335851.

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Burkhardt, Brian. "THE FUTURE OF ELECTROCARDIOGRAPH TELEMETRY SYSTEMS." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605770.

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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California<br>The Electrocardiograph (EKG or ECG) measures electrical changes of tissue surrounding the heart to create a time-based representation of the physical operation of the heart. The purpose of this paper is to explore the future of ECG telemetry systems and how they are used in health care. The initial goal is to develop an inexpensive, efficient, and robust real-time ECG telemetry system. The future goal is to create a wireless network of miniature body sensors capable of meas
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Murray, Henry G. "Evaluation of the fetal electrocardiogram (ECG)." Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297895.

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Davies, Alan. "Examining expertise through eye movements : a study of clinicians interpreting electrocardiograms." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/examining-expertise-through-eye-movements-a-study-of-clinicians-interpreting-electrocardiograms(43593216-6887-43ab-a404-00b00fa38e12).html.

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The electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart. The 12-lead ECG shows this activity in 12 "views" called "leads", relative to the location of sensors attached to the body surface. The ECG is a routinely applied cost effective diagnostic medical test, utilised in healthcare settings around the world. Although more than three hundred million ECGs are recorded each year, correctly interpreting them is considered a complex task. Failure to make correct interpretations can lead to injury or death and costs vast sums in litigation payments. Many aut
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Delano, Margaret K. "A long term wearable electrocardiogram (ECG) measurement system." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76811.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 101-102).<br>In this thesis, a low-power, wearable monitoring system was developed from discrete electronic components and custom PCBs. The device was designed to maximize comfort and minimize the footprint on the user. A single le
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Michalek, Paul. "AN AUTHENTIC ECG SIMULATOR." Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2500.

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An ECG (electrocardiogram) simulator is an electronic tool that plays an essential role in the testing, design, and development of ECG monitors and other ECG equipment. Principally an ECG simulator provides ECG monitors with an electrical signal that emulates the human heart's electrical signal so that the monitor can be tested for reliability and important diagnostic capabilities. However, the current portable commercially available ECG simulators are lacking in their ability to fully test ECG monitors. Specifically, the portable simulators presently on the market do not produce authentic ECG
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Junttila, J. (Juhani). "Characteristics of subjects with Brugada syndrome type electrocardiogram." Doctoral thesis, University of Oulu, 2008. http://urn.fi/urn:isbn:9789514287701.

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Abstract Brugada syndrome is an inherited arrhythmia disorder that predisposes to sudden cardiac death. It is characterized by its distinct ECG pattern. The purpose of this thesis was to study the phenotype and genotype characteristics of subjects with Brugada syndrome type ECG. The first study population consisted of 2479 young male Air Force applicants and 542 healthy middle-aged subjects. The 12-lead ECG was analyzed to assess the prevalence and prognosis of Brugada pattern in Finnish population. The second population consisted of 168 patients with AF. The ECGs of the patients with family
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Prescott, Simon L. "Nonlinear dynamical analysis of electrocardiogram data and the prospects for control of cardiac chaos." Thesis, Keele University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302274.

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Books on the topic "Electrocardiograms (ECGs)"

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Araoye, Matthew Akinyemi. The normal electrocardiogram (ECG) in adult Nigerians. University of Ilorin, 1986.

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V, Veena. ECG Book: About Electrocardiograms Third Edition. Blurb, 2019.

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ECG Interpretation: An Incredibly Easy! Workout. LWW, 2008.

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Flip and See ECG. Mosby, 2011.

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Flip and See ECG. Elsevier - Health Sciences Division, 2008.

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Flip and See ECG. Elsevier - Health Sciences Division, 2012.

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Luna, Antonio Bayés de. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0069.

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The electrocardiogram (ECG) is one of the simplest, inexpensive, safest, and reproducible techniques that exist in medicine. During the current renaissance of electrocardiography, we need to improve how to interpret an ECG. This is especially important because currently the automatic interpretation still needs medical supervision. In spite of the great relevance of the ECG in the diagnosis of heart diseases, this section also includes a chapter that expresses the limitations of the ECG, entitled ‘The abnormal ECG without apparent heart disease and the normal ECG in serious heart disease: two e
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Archer, Nick, and Nicky Manning. Fetal cardiac rhythm. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199230709.003.0013.

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Introduction 166Identification of cardiac rhythm 168Normal rhythms 172Fast abnormal rhythms 184Slow abnormal rhythms 194Irregular rhythms 200Normal cardiac rhythm originates in the sinus node, a RA structure. Atrial electrical depolarization is manifest on the electrocardiogram (ECG) by a P wave and is followed by atrial contraction....
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validaremos, validaremos. ECG A-Z: Electrocardiogramme Pour les débutants, Schémas et Cas Cliniques. Format A4. Independently Published, 2021.

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Sabharwal, Nikant, Chee Yee Loong, and Andrew Kelion. Introduction to nuclear cardiology. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199206445.003.0001.

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Introduction 2Important milestones 4Relation to other imaging modalities 6The cardiologist of the early twenty-first century takes for granted the wide range of imaging modalities at his/her disposal, but it was not always so. At the beginning of the 1970s, invasive cardiac catheterization was the only reliable cardiac imaging technique. Subsequently, nuclear cardiology investigations led the way in the non-invasive assessment of cardiac disease. Some of the principles underlying these investigations [e.g. electrocardiogram (ECG)-triggered gating] have also been of great importance in the deve
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Book chapters on the topic "Electrocardiograms (ECGs)"

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Pérez, Álvaro José Bocanegra, Matthew J. Magoon, Manisha Sahota, et al. "Non-Invasive Detection of Fetal Ischemia Through Electrocardiography." In Computational Physiology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53145-3_4.

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AbstractDuring pregnancy, fetal distress requiring clinical intervention can be difficult to accurately monitor and diagnose, necessitating technological improvements to bring clear information to patients and their care teams. Non-invasive fetal Electrocardiography (NI-fECG) monitoring may allow for earlier and more reliable detection of global cardiac ischemia due to hypoxia. However, the lowsignal-to-noise ratio of the fetal heartbeat relative to the maternal heartbeat remains a challenge. To enable reliable recognition of ischemia in NI-fECG, we propose an approach that combines simulating
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Yokoyama, Kiyoko. "Electrocardiogram (ECG)." In Current Topics in Environmental Health and Preventive Medicine. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-2160-7_3.

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Kusumoto, Fred. "The Normal Electrocardiogram." In ECG Interpretation. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40341-6_3.

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Rautaharju, Pentti M. "ECG Predictors of Sudden Cardiac Death." In The Female Electrocardiogram. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15293-6_6.

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Rautaharju, Pentti M. "ECG Predictors of Atrial Fibrillation and Heart Failure." In The Female Electrocardiogram. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15293-6_5.

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Hombach, V. "Ambulatory Electrocardiogram Monitoring." In Specialized Aspects of ECG. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-880-5_1.

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Singh, Neeraj Kumar. "Formalisation of Electrocardiogram (ECG)." In Using Event-B for Critical Device Software Systems. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5260-6_10.

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Herlitz, Johan, Leif Svensson, and Per Johansson. "The Pre-Hospital Electrocardiogram." In Specialized Aspects of ECG. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-880-5_2.

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Sörnmo, Leif, Elin Trägårdh Johansson, and Michael B. Simson. "The Signal-Averaged Electrocardiogram." In Specialized Aspects of ECG. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-880-5_7.

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Kusumoto, Fred M. "The normal electrocardiogram." In ECG Interpretation: From Pathophysiology to Clinical Application. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-88880-4_3.

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Conference papers on the topic "Electrocardiograms (ECGs)"

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Hassan, Tayyib Ul, and Hasan Fakhar. "CMBT Net: A Deep Learning Based Approach for Automatic Feature Extraction and Classification of Electrocardiograms (ECGs)." In 2024 International Conference on Frontiers of Information Technology (FIT). IEEE, 2024. https://doi.org/10.1109/fit63703.2024.10838427.

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Lin, Jia Lun, Jin Yang Xia, and Xiao Ling Li. "Research on a CNN Based Clinical Electrocardiogram Classification Model." In 12th Annual International Conference on Material Science and Engineering. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-fj7a4x.

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Electrocardiogram (ECG) is the most commonly used diagnostic method for heart diseases such as arrhythmia. However, its inherent complexity, to some extent, reduces the accuracy of diagnosis. To quickly and automatically identify the type of arrhythmia, this paper constructs a clinical ECG classification model based on Convolutional Neural Network (CNN) to assist clinicians in analyzing ECG signals. The MIT-BIH ECG database is used as the research data source, and the heart beats are classified into 5 categories based on AAMI EC57 standard. 95% of the ECG data is randomly divided into training
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Li, Lujundong, Tong Xia, Haojie Zhang, et al. "ECG-DPM: Electrocardiogram Generation via a Spectrogram-based Diffusion Probabilistic Model." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00074.

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Dias, Felipe M., Estela Ribeiro, Quenaz B. Soares, Jose E. Krieger, and Marco A. Gutierrez. "Predicting Age and Sex from Reduced Lead Electrocardiograms using Deep Learning." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/sbcas.2025.7705.

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Artificial intelligence is increasingly used to extract health insights from 12-lead (12L) electrocardiograms (ECG). Here, we propose a deeplearning model to predict sex and age from 12L and reduced-lead ECGs (6L–1L) and assess their impact on mortality risk. Using a ResNeXt-based model trained on the CODE15 dataset, our best models achieved an F1-score of 0.800 for sex classification (12L) and a mean absolute error of 8.961 for age estimation (4L). We found that overestimated age predictions and incorrect sex classifications were associated with higher mortality risk, whereas underestimated a
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MAHAMAT, Hassan Adam, Sabir Jacquir, Cliff Khalil, Gabriel Laurent, and Stephane Binczak. "Automatic Detection of the Wolff:Parkinson:White (WPW) Syndrome from Electrocardiograms (ECGs)." In 2016 Computing in Cardiology Conference. Computing in Cardiology, 2016. http://dx.doi.org/10.22489/cinc.2016.120-278.

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Yao, Z., Y. Dong, and W. K. Jenkins. "Experimental evaluations of sequential adaptive processing for fetal electrocardiograms (ECGs)." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421238.

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Ohkura a, Michiko, and Yosuke Yamasaki b. "Evaluation of Kawaii Size by Measuring ECG." In Applied Human Factors and Ergonomics Conference. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe100550.

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Kawaii, which represents an affective value, includes such positive meanings as cute, lovable, and small. In the 21st century, the affective values of industrial products are becoming more important. However, since few studies have focused on kawaii attributes, we are systematically analyzing kawaii products themselves: the kawaii feelings evoked by the shapes, colors, sizes, textures and tactile sensations caused by the materials of such products. In this paper, we refocused on kawaii sizes and performed experiments using both Augmented Reality and Virtual Reality to evaluate a virtual object
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Dong, Y., J. Kovarskiy, and W. K. Jenkins. "The addition of adaptive comb filtering to sequential adaptive processing for fetal electrocardiograms (ECGs)." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869655.

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Méndez Rubio, E., N. Castañeda Villa, and A. Jiménez González. "Extracción del electrocardiograma fetal mediante técnicas de separación ciega de fuentes: una implementación para registros abdominales de cuatro canales." In XLI Congreso Nacional de Ingeniería Biomédica. Sociedad Mexicana de Ingeniería Biomédica, 2018. http://dx.doi.org/10.24254/cnib.18.1.

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En este trabajo se estudia una versión modificada del Análisis por Componentes Independientes (MICA) para procesar registros de ECG abdominal (ECGa) de 4 canales y extraer el ECG fetal (ECGf). Usando una base de datos de Physionet con 5 registros compuestos de ECGa y ECG directo, el MICA se aplicó con base en tres algoritmos de ICA (FastICA, Infomax y TDSep) y, en los ECGf extraídos, se calcularon la relación señal interferencia (SIR) y el error de medición del intervalo R-R. Los resultados indican que FastICA tiene el mejor desempeño en la extracción del ECGf y en la medición de la frecuencia
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Hu, Yaojun, Jintai Chen, Lianting Hu, et al. "Personalized Heart Disease Detection via ECG Digital Twin Generation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/649.

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Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital tw
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Reports on the topic "Electrocardiograms (ECGs)"

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Weng, JIeqiong, Jingfang Zhang, Ke Xu, et al. Efficacy of Shexiang Baoxin Pills Combined with Statins on Blood Lipid Profile in Patients with Coronary Heart Disease: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0100.

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Review question / Objective: P（Population) ：Patients with coronary heart disease; I（Intervention) : Statins treatment in combination with Shexiang Baoxin pill; C（Comparison): Statins alone; O（Outcome）: Improvement of symptoms and blood lipids; S（Study design）：Clinical randomized trials. Eligibility criteria: To be included, trials were required to meet the following criteria: (1) patients were included in the studies according to diagnostic criteria of coronary heart disease established by the WHO, InternationalSociety of Cardiology and Association (ISCA), Internal Medicine, 7th edition ( IM-7
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Palomino Potes, Heidys, and Catalina Olaya-López. Rol de enfermería en las alteraciones electrocardiográficas. Ediciones Universidad Cooperativa de Colombia, 2021. http://dx.doi.org/10.16925/gcgp.39.

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Abstract:
El profesional de enfermería que se desempeña en el ámbito clínico o comunitario se enfrenta diariamente a la atención de personas con múltiples alteraciones en su estado de salud, lo cual requiere acciones complejas de cuidado. En muchas ocasiones, el equipo interdisciplinario de enfermería es el primero en detectar una alteración electrocardiográfica y su actuación puede ser fundamental en la evolución del paciente. El electrocardiograma ECG de 12 derivaciones es una herramienta útil para el diagnóstico de síndromes coronarios agudos y arritmias en el servicio de urgencias. Cada alteración e
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Madsen, Jens, Nikhil Kuppa, and Lucas Parra. The Brain, Body, and Behaviour Dataset - Neural Engineering Lab, CCNY. Fcp-indi, 2025. https://doi.org/10.15387/fcp_indi.retro.bbbd.

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When humans engage with video, their brain and body interact in response to sensory input. To investigate these interactions, we recorded and are releasing a dataset from N=178 participants across five experiments featuring short online educational videos. This dataset comprises approximately 110 hours of multimodal data including electrocardiogram (ECG), heart rate, respiration, breathing rate, pupil size, electrooculogram (EOG), gaze position, saccades, blinks, fixations, head movement, and electroencephalogram (EEG). Participants viewed 3-6 videos (mean total duration: 28±5 min) to test att
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