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Journal articles on the topic 'Electrocardiograms (ECGs)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Winkler, Benjamin, Claudia Nagel, Nando Farchmin, et al. "Global Sensitivity Analysis and Uncertainty Quantification for Simulated Atrial Electrocardiograms." Metrology 3, no. 1 (2022): 1–28. http://dx.doi.org/10.3390/metrology3010001.

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The numerical modeling of cardiac electrophysiology has reached a mature and advanced state that allows for quantitative modeling of many clinically relevant processes. As a result, complex computational tasks such as the creation of a variety of electrocardiograms (ECGs) from virtual cohorts of models representing biological variation are within reach. This requires a correct representation of the variability of a population by suitable distributions of a number of input parameters. Hence, the assessment of the dependence and variation of model outputs by sensitivity analysis and uncertainty

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Desai, Vishal, and Dinesh Dave. "Is artificial intelligence better than manual methods in diagnosis of electrocardiograms (ECGs) or not?" International Journal of Advances in Medicine 4, no. 5 (2017): 1463. http://dx.doi.org/10.18203/2349-3933.ijam20174304.

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Background: Now artificial intelligence is used extensively to diagnose ECGs. Artificial intelligence is the point where the doctors and engineers meet to decrease the misdiagnosis of cardiac diseases. So, we thought it worthwhile to compare and contrast the ECG diagnosis by artificial intelligence and skilled physicians. This paper exposes the potential of diagnosis of each ECG by artificial intelligence and skilled physicians.Methods: The research was done on 30 ECGs and their diagnosis was compared by both the methods.Results: The result was divided into 3 categories: absolutely misdiagnose

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van der Zande, Joske, Marc Strik, Rémi Dubois, et al. "Using a Smartwatch to Record Precordial Electrocardiograms: A Validation Study." Sensors 23, no. 5 (2023): 2555. http://dx.doi.org/10.3390/s23052555.

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Smartwatches that support the recording of a single-lead electrocardiogram (ECG) are increasingly being used beyond the wrist, by placement on the ankle and on the chest. However, the reliability of frontal and precordial ECGs other than lead I is unknown. This clinical validation study assessed the reliability of an Apple Watch (AW) to obtain conventional frontal and precordial leads as compared to standard 12-lead ECGs in both subjects without known cardiac anomalies and patients with underlying heart disease. In 200 subjects (67% with ECG anomalies), a standard 12-lead ECG was performed, fo

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Bogdanov, M. R., G. R. Shakhmametova, I. Sh Shaibakov, and N. N. Oskin. "Opportunities to Reduce the Risk of Cardiovascular Death by Improving Machine Learning Methods." Informacionnye Tehnologii 30, no. 3 (2024): 159–67. http://dx.doi.org/10.17587/it.30.159-167.

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Improving algorithms for automatic recognition of electrocardiograms requires increasing of training dataset, which is not always possible due to the rarity of certain cardiac diseases or ethical issues. It is possible to improve the algorithms for generating synthetic electrocardiograms using mechanistic models and generative-descriptive neural networks (GANs). At the same time, when evaluating the effectiveness of the proposed solutions, various authors offer different quality assessment metrics from subjective expert assessment to the squared mean error. We compare two approaches to generat

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McLaren, J., A. Taher, and L. Chartier. "LO33: Sharing and teaching electrocardiograms to minimize infarction." CJEM 22, S1 (2020): S19. http://dx.doi.org/10.1017/cem.2020.89.

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Background: Every 30-minute delay to ST-Elevation Myocardial Infarction (STEMI) reperfusion increases one-year mortality by 7.5%. A local audit found that the third of patient electrocardiograms (ECGs) not initially meeting classic STEMI criteria had an ECG-to-Activation (ETA) time of over 90 minutes, more than five times that of classic STEMIs. However, three quarters of “STEMI negative” ECGs met STEMI-equivalent patterns or rules for subtle occlusion, uncovering an opportunity for improvement. Aim Statement: We aimed to reduce ETA time, from initial emergency department (ED) ECG to activatio

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Schaffer, Jeffrey A., Terence D. Valenzuela, Arthur L. Wright, et al. "Emergency Physician Interpretation of Prehospital, Paramedic-Acquired Electrocardiograms." Prehospital and Disaster Medicine 7, no. 3 (1992): 251–55. http://dx.doi.org/10.1017/s1049023x00039583.

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AbstractHypothesis:Emergency physician interpretation of prehospital, paramedic-acquired, electrocardiograms (ECG) is accurate judged by comparison with that of a reference cardiologist.Methods:Twelve-lead ECGs were obtained by paramedics in the field from 150 patients with acute chest pain. The ECGs were transmitted by cellular telephone to a central location. Each ECG was assessed for evidence of acute myocardial infarction (AMI) by: 1) a third-year, emergency medicine resident (EMP-R); 2) a residency-trained, board-certified, emergency physician (EMP-RT); 3) an emergency physician board cer

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Solomons, Luke, Adrian Treloar, and Ryan Noronha. "Competence of psychiatric clinicians in interpreting electrocardiograms and QT intervals: can they do this? Does it matter?" Psychiatric Bulletin 32, no. 8 (2008): 291–94. http://dx.doi.org/10.1192/pb.bp.107.017715.

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Aims and MethodWe assessed the abilities of trainee and consultant psychiatrists in reading and interpreting electrocardiograms (ECGs) and QT intervals using a questionnaire and standardised ECG.ResultsOnly 5% of our sample of trainee and consultant psychiatrists could correctly indicate a QTc interval. Performances on other measures, such as rate were also poor, with senior house officers performing better than consultants.Clinical ImplicationsThe increased awareness of problems caused by antipsychotics has not been reflected in improved knowledge of ECGs among psychiatrists. Machines do not

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Takada, Mai. "Research for the Working Theorem and Application of Electrocardiogram." Transactions on Computer Science and Intelligent Systems Research 7 (November 25, 2024): 410–14. https://doi.org/10.62051/f4c1n817.

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Electrocardiograms (ECGs) are essential for diagnosing cardiac diseases, and recording the heart's electrical activity across cycles. Despite their critical role, there remains a gap in understanding the detailed principles and historical development of ECGs. This article introduces the historical development, basic principles, and application of ECG in the diagnosis of heart diseases. The working principle of ECG is studied, including the depolarization and repolarization process of myocardial cells, and how these physiological activities are manifested in ECG waveforms. The research involves

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Karim, Shifa R., Hans C. Helseth, Peter O. Baker, et al. "Artificial Intelligence Detection of Occlusive Myocardial Infarction from Electrocardiograms Interpreted as “Normal” by Conventional Algorithms." Journal of Personalized Medicine 15, no. 4 (2025): 130. https://doi.org/10.3390/jpm15040130.

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Background: Some authors advocate that ECGs with conventional computer algorithm (CCA) interpretations of “normal” need not be immediately reviewed. However, such ECGs may actually manifest findings of acute coronary occlusion myocardial infarction (OMI). We sought to determine if such cases can be detected by artificial intelligence (AI). Methods: We studied a retrospective series (2014–2024) of cases with ≥1 pre-angiography ECGs with a proven OMI outcome with a CCA ECG interpretation of “normal”. The OMI outcome was defined as (1) the diagnosis of acute type I MI, (2) an angiographic culprit

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Moutaib, Mohammed, Mohammed Fattah, Yousef Farhaoui, Badraddine Aghoutane, and Moulhime El Bekkali. "Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks." Data and Metadata 2 (December 30, 2023): 113. http://dx.doi.org/10.56294/dm2023113.

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Predicting fetal and maternal electrocardiograms (ECGs) is crucial in advanced prenatal monitoring. In this study, we explore the effectiveness of Convolutional Neural Networks (CNNs), using a carefully developed methodology to predict the category of fetal (F) or maternal (M) ECGs. In the first part, we trained a CNN model to predict fetal and maternal ECG images. In the following sections, the study results will be revealed. The CNN model demonstrated its ability to effectively discriminate between fetal and maternal patterns using automatically learned features.

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Dubatovka, Alina, and Joachim M. Buhmann. "Automatic Detection of Atrial Fibrillation from Single-Lead ECG Using Deep Learning of the Cardiac Cycle." BME Frontiers 2022 (May 2, 2022): 1–12. http://dx.doi.org/10.34133/2022/9813062.

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Objective and Impact Statement. Atrial fibrillation (AF) is a serious medical condition that requires effective and timely treatment to prevent stroke. We explore deep neural networks (DNNs) for learning cardiac cycles and reliably detecting AF from single-lead electrocardiogram (ECG) signals. Introduction. Electrocardiograms are widely used for diagnosis of various cardiac dysfunctions including AF. The huge amount of collected ECGs and recent algorithmic advances to process time-series data with DNNs substantially improve the accuracy of the AF diagnosis. DNNs, however, are often designed as

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Ryu, Ji Seung, Solam Lee, Yuseong Chu, et al. "Deep Learning Algorithms for Estimation of Demographic and Anthropometric Features from Electrocardiograms." Journal of Clinical Medicine 12, no. 8 (2023): 2828. http://dx.doi.org/10.3390/jcm12082828.

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The electrocardiogram (ECG) has been known to be affected by demographic and anthropometric factors. This study aimed to develop deep learning models to predict the subject’s age, sex, ABO blood type, and body mass index (BMI) based on ECGs. This retrospective study included individuals aged 18 years or older who visited a tertiary referral center with ECGs acquired from October 2010 to February 2020. Using convolutional neural networks (CNNs) with three convolutional layers, five kernel sizes, and two pooling sizes, we developed both classification and regression models. We verified a classif

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Versaci, Mario, Giovanni Angiulli, and Fabio La Foresta. "A Modified Heart Dipole Model for the Generation of Pathological ECG Signals." Computation 8, no. 4 (2020): 92. http://dx.doi.org/10.3390/computation8040092.

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In this paper, we introduce a new dynamic model of simulation of electrocardiograms (ECGs) affected by pathologies starting from the well-known McSharry dynamic model for the ECGs without cardiac disorders. In particular, the McSharry model has been generalized (by a linear transformation and a rotation) for simulating ECGs affected by heart diseases verifying, from one hand, the existence and uniqueness of the solution and, on the other hand, if it admits instabilities. The results, obtained numerically by a procedure based on a Four Stage Lobatto IIIa formula, show the good performances of t

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Zhang, Yichu, Siqi Guo, Ju Tian, et al. "A deep learning model could screen for coronary heart disease from a “pseudo-normal” electrocardiogram." Medicine 104, no. 24 (2025): e42764. https://doi.org/10.1097/md.0000000000042764.

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Background: This study aimed to develop a deep learning model (DLM) for rapid screening of coronary heart disease (CHD) using “pseudo-normal” electrocardiograms (ECGs), particularly focusing on patients who present with normal or near-normal ECGs at admission. Methods: This study utilized standard 12-lead ECGs from CHD and non-CHD patients collected at the Second Affiliated Hospital of Nanchang University (SAH) between September 2017 and May 2019. These data sets were employed for training and validation of the DLM. For external testing, ECGs from CHD patients who underwent revascularization f

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Kotas, M., J. M. Leski, and J. Wrobel. "Sequential separation of twin pregnancy electrocardiograms." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 1 (2016): 91–101. http://dx.doi.org/10.1515/bpasts-2016-0011.

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Abstract We propose to tackle the problem of maternal abdominal electric signals decomposition with a combined application of independent component analysis and projective or adaptive filtering. The developed method is employed to process the four-channel abdominal signals recorded during twin pregnancy. These signals are complicated mixtures of the maternal ECG, the ECGs of the fetal twins and noise of various origin. Although the independent component analysis cannot separate the respective signals, the proposed combination of the methods deals with this task successfully. A simulation exper

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Phillips, James D., Tyler Merrill, J. Reed Gardner, et al. "Clinical Significance of Screening Electrocardiograms for the Administration of Propranolol for Problematic Infantile Hemangiomas." International Journal of Pediatrics 2021 (February 23, 2021): 1–5. http://dx.doi.org/10.1155/2021/6657796.

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Objective. Low-dose nonselective β blockade is an effective treatment for problematic infantile hemangioma (PIH). Screening electrocardiograms (ECG) are performed prior to the initiation of propranolol to minimize the risk of exacerbating undiagnosed heart block. How ECG results affect subsequent propranolol usage and patient management remains unclear. We examined the value of ECG prior to propranolol therapy in a quaternary pediatric hospital. Methods. A retrospective chart review was performed on all infants who received propranolol (2 mg/kg/day divided three times daily) to treat PIH at Ar

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Wyld, Luke. "Transport decisions: acute coronary syndrome and high-risk electrocardiograms." Journal of Paramedic Practice 16, no. 3 (2024): 1–6. http://dx.doi.org/10.12968/jpar.2024.16.3.cpd1.

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Patients experiencing a high-risk non-ST-elevation myocardial infarction are likely to benefit from prehospital medical contact, transport to a percutaneous coronary intervention (PCI) centre and early definitive treatment. They may be provisionally diagnosed through the identification of a high-risk electrocardiogram (ECG). Advances in ECG interpretation have led to the identification of a cohort of at-risk patients whose treatment may previously have been delayed. Patients with certain ECG changes are considered high risk and may be experiencing coronary occlusions. Early PCI and direct tran

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Sprenger, Nora, Alireza Sepehri Shamloo, Jonathan Schäfer, et al. "Feasibility and Reliability of Smartwatch to Obtain Precordial Lead Electrocardiogram Recordings." Sensors 22, no. 3 (2022): 1217. http://dx.doi.org/10.3390/s22031217.

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The Apple Watch is capable of recording single-lead electrocardiograms (ECGs). To incorporate such devices in routine medical care, the reliability of such devices to obtain precordial leads needs to be validated. The purpose of this study was to assess the feasibility and reliability of a smartwatch (SW) to obtain precordial leads compared to standard ECGs. We included 100 participants (62 male, aged 62.8 ± 13.1 years) with sinus rhythm and recorded a standard 12-lead ECG and the precordial leads with the Apple Watch. The ECGs were quantitively compared. A total of 98 patients were able to re

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Heath, Joseph, Maroulla Anderson, and Jonathan Miles-Stokes. "A closed audit reviewing the electrocardiograms of patients presenting to the memory assessment team." BJPsych Open 7, S1 (2021): S30. http://dx.doi.org/10.1192/bjo.2021.133.

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AimsTo review the ECGs of all patients referred to MAT services over the preceding 5 year period.BackgroundNeurodegenerative conditions such as Alzheimer's Disease can be treated with Acetylcholinesterase Inhibitors (AChEI) to slow down cognitive decline. Side effects of AChEIs include bradycardia, syncope and cardiac conduction disorders. An electrocardiograms (ECG) is completed prior to memory assessment team (MAT) medical assessments to screen for those who may be at risk of the cardiac side effects of AChEIs. ECGs may be included in the initial referral to the service or completed by the M

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Tahri Sqalli, Mohammed, Dena Al-Thani, Mohamed B. Elshazly, Mohammed Al-Hijji, Alaa Alahmadi, and Yahya Sqalli Houssaini. "Understanding Cardiology Practitioners’ Interpretations of Electrocardiograms: An Eye-Tracking Study." JMIR Human Factors 9, no. 1 (2022): e34058. http://dx.doi.org/10.2196/34058.

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Background Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners’ visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately. O

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Naing, A., H. Veasey-Rodrigues, D. S. Hong, et al. "Electrocardiograms (ECGs) in phase I anticancer drug development: the MD Anderson Cancer Center experience with 8518 ECGs." Annals of Oncology 23, no. 11 (2012): 2960–63. http://dx.doi.org/10.1093/annonc/mds130.

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Hamlin, Robert. "Making subjective interpretation of electrocardiograms (ECGs) more facile: A novel method of presenting and assessing serial ECGs." Journal of Pharmacological and Toxicological Methods 70, no. 3 (2014): 336–37. http://dx.doi.org/10.1016/j.vascn.2014.03.095.

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Zhao, Yi, and Song-Kyoo Kim. "Versatile Machine Learning-Based Authentications by Using Enhanced Time-Sliced Electrocardiograms." Information 15, no. 4 (2024): 187. http://dx.doi.org/10.3390/info15040187.

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This paper addresses the enhancement of modern security through the integration of electrocardiograms (ECGs) into biometric authentication systems. As technology advances, the demand for reliable identity authentication systems has grown, given the rise in breaches associated with traditional techniques that rely on unique biological and behavioral traits. These techniques are emerging as more reliable alternatives. Among the biological features used for authentication, ECGs offer unique advantages, including resistance to forgery, real-time detection, and continuous identification ability. A

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Grauel, Adolf, Lars A. Ludwig, and Georg Klene. "ECG Diagnostics by Fuzzy Decision Making." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, no. 02 (1998): 201–10. http://dx.doi.org/10.1142/s0218488598000185.

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The analysis of electrocardiograms (ECGs) helps physicians make their cardiac diagnosis. Therefore a large store of medical knowledge and practical experience is required. In this paper we report on our investigations of a rule-based fuzzy logic system that processes ECG data using the knowledge of a medical expert. The aim is to give support to the physician for his diagnosis. In this first consideration we discuss single modules of the rule-based system proposed and moreover we present the used input and output variables of the rulebases. The performance of the implemented rule-based fuzzy l

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Ghosh, Soumya, Anil Kodagalli, Faiz Bhatti, and Timothy Bradbeer. "Monitoring electrocardiograms of service users on high-dose methadone substitution therapy: an audit." Psychiatrist 34, no. 11 (2010): 489–91. http://dx.doi.org/10.1192/pb.bp.109.027813.

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Aims and methodTo explore the extent to which 2007 Department of Health guidelines on monitoring of high-dose (⩾100 mg) methadone were followed, the reasons for non-adherence to these and the prevalence of QTc prolongation. We developed a simple tool for collecting data from case notes.ResultsOut of 25 service users, 11 had had an electrocardiogram (ECG) and 7 had evidence of requests sent to general practitioners. After implementation of our recommendations, ten more service users had ECGs within 1 month. All but one ECG was normal. Methadone prescribing in favour of lower doses has been obse

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MALONEY, MICHAEL J., and JEFFREY S. SCHWAM. "In Reply: Clonidine and Electrocardiograms." Pediatrics 98, no. 6 (1996): 1224. http://dx.doi.org/10.1542/peds.98.6.1224.

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We appreciate Drs Blackman, Samson-Fang, and Gutgesell's interest in our letter to the editor and the opportunity to respond to some of their comments. Pediatricians and child psychiatrists are frequently prescribing the combination of clonidine and methylphenidate to manage children with attention deficit hyperactivity disorder who are unresponsive to other treatments. We agree that monitoring pulse and blood pressure, as well as looking for a history of cardiac sinus node dysfunction, makes clinical sense. Regarding baseline ECGs, there does not seem to be enough data on the use of this comb

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Barbic, D., C. Vadeanu, B. E. Grunau, K. Ramanathan, and F. X. Scheuermeyer. "MP034: What is the diagnostic accuracy of Canadian emergency physicians and cardiologists interpreting potential acute ST-elevation myocardial infarction (STEMI) electrocardiograms?" CJEM 18, S1 (2016): S77—S78. http://dx.doi.org/10.1017/cem.2016.175.

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Introduction: The accurate interpretation of potential ST-segment elevations on electrocardiograms (ECGs) to diagnose acute myocardial infarction (MI) is a critical competency for emergency physicians (EPs) and cardiologists. There is conflicting evidence on the diagnostic accuracy of EPs and cardiologists interpreting potential STEMI ECGs. Methods: We conducted a web-based assessment of the diagnostic accuracy of potential STEMI ECGs of Canadian EPs and cardiologists. They were identified using the membership lists of the Canadian Association of Emergency Physicians and the academic departmen

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Henderson, Tom, Dominic Gallagher, and Cameron Stark. "A survey of the use of the electrocardiogram in psychiatry." Psychiatric Bulletin 21, no. 3 (1997): 136–38. http://dx.doi.org/10.1192/pb.21.3.136.

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A survey was undertaken to ascertain if doctors working in psychiatry knew when to perform electrocardiograms (ECGs) prior to and during psychiatric treatments where indicated, and to investigate the training, confidence and ability of doctors in ECG interpretation. Half had received no training in the indications for performing an ECG. Opinion differed widely as to when to perform an ECG, with 54% stating an ECG was required before treatment with pimozide and only 19% and 11% stating ECGs were required during the course of treatment with pimozide and high dose antipsychotic medication respect

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Meral, I., Y. Tekintangac, and H. Demir. "Effects of 900 MHz electromagnetic field emitted by cellular phones on electrocardiograms of guinea pigs." Human & Experimental Toxicology 33, no. 2 (2013): 164–69. http://dx.doi.org/10.1177/0960327113482596.

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This study was carried out to determine the effects of electromagnetic field (EMF) emitted by cellular phones (CPs) on electrocardiograms (ECGs) of guinea pigs. A total of 30 healthy guinea pigs weighing 500–800 g were used. After 1 week of adaptation period, animals were randomly divided into two groups: control group ( n = 10) and EMF-exposed group ( n = 20). Control guinea pigs were housed in a separate room without exposing them to EMFs of CPs. Animals in second group were exposed to 890–915 MHz EMF (217 Hz of pulse rate, 2 W of maximum peak power and 0.95 wt kg−1 of specific absorption ra

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Dahiya, Ekta Singh, Anubha Manju Kalra, Andrew Lowe, and Gautam Anand. "Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review." Sensors 24, no. 4 (2024): 1318. http://dx.doi.org/10.3390/s24041318.

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In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive sea

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Abdulbaqi, Azmi Shawkat, Israa Falih Muslim, Asraa A. Abd Al-Ameer, and Ahmed J. Obaid. "Healthcare surveillance based on cloud computing utilizing mobile devices." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 4 (2023): 1189–96. http://dx.doi.org/10.47974/jdmsc-1566.

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Cardiologists utilized electrocardiograms (ECGs) to observe cardiac disease in everyday life. A system for reading ECGs outside the hospital based on a Mobile Instrument (MobileInst) was developed to make it easier for specialists to observe their patients’ ECGs. A MobileInst receives an ECG signal and sends the signals to an ECG instrument based on the proposed system. By using MobileInst, ECG equipment alarms can be detected. A Cloud Alarm Service (SCA), which records ECG signaling and alert information, receives alarm information when MobileInst receives alarm signals. MobileInst displays t

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Wahib, SMA, AEMM Islam, MM Haque, et al. "Comparative Study between 12 and 15 Lead Electrocardiograms for Evaluation of Acute Posterior Myocardial Infarction." Cardiovascular Journal 4, no. 2 (2012): 153–63. http://dx.doi.org/10.3329/cardio.v4i2.10460.

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Background: The ECG diagnosis of acute posterior infarction has traditionally been based on the presence of ST segment depression on the pericardial chest leads. However, such ST segment depression is neither specific nor sensitive for the diagnosis of a posterior infarction. Detection of ST segment elevation in V7- V9 in 15 lead ECG can help in early diagnosis of acute Posterior myocardial infarction. Method: Total of 100 patients Inferior myocardial infarction was evaluated by Electrocardiography in standard 12 lead and 15 lead. The patients were categorized into group I having posterior/ an

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Dr., Deepak NR, Prasad Ashansh, BE Harsh, B. Shurthi, Moond Himanshu, and Sharma Chekit. "Machine Learning for Early Detection of Heart Disease." Journal of Research and Review: Hacking Techniques and Information Security Systems 1, no. 1 (2025): 33–38. https://doi.org/10.5281/zenodo.15195374.

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<em>Heart disease continues to be a leading cause of death globally, with early detection playing a critical role in improving outcomes and preventing severe complications. Traditional diagnostic methods, such as electrocardiograms (ECGs), echocardiograms, and stress tests, often detect conditions at later stages, when intervention becomes more challenging. This report explores the role of machine learning in early heart disease detection, highlighting various ML techniques such as supervised learning, deep learning, and unsupervised learning. These models can identify patterns and correlation

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Zhuang, Shuxin, Fenlan Li, Zhemin Zhuang, Wenbin Rao, Alex Noel Joseph Raj, and Vijayarajan Rajangam. "Improved ECG-Derived Respiration Using Empirical Wavelet Transform and Kernel Principal Component Analysis." Computational Intelligence and Neuroscience 2021 (October 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/1360414.

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Many methods have been developed to derive respiration signals from electrocardiograms (ECGs). However, traditional methods have two main issues: (1) focusing on certain specific morphological characteristics and (2) not considering the nonlinear relationship between ECGs and respiration. In this paper, an improved ECG-derived respiration (EDR) based on empirical wavelet transform (EWT) and kernel principal component analysis (KPCA) is proposed. To tackle the first problem, EWT is introduced to decompose the ECG signal to extract the low-frequency part. To tackle the second issue, KPCA and pre

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Haber, Tobias, Pascal Striebel, Bruno Ismer, and Juraj Melichercik. "Plug-in circuit board for the Raspberry-Pi microcomputer to reproduce multi-channel original electrocardiograms." Current Directions in Biomedical Engineering 5, no. 1 (2019): 561–64. http://dx.doi.org/10.1515/cdbme-2019-0141.

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AbstractCommercial simulators can only reproduce electrocardiograms (ECG) of the normal and diseased heart rhythm in a simplified waveform and with a low number of channels. With the presented project, the variety of digitally archived ECGs, recorded during electrophysiological examinations, should be made usable as original analogue signals for research and teaching purposes by the development of a special printed circuit board for the minicomputer “Raspberry-Pi “.

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Di Costanzo, Assunta, Carmen Anna Maria Spaccarotella, Giovanni Esposito, and Ciro Indolfi. "An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review." Journal of Clinical Medicine 13, no. 4 (2024): 1033. http://dx.doi.org/10.3390/jcm13041033.

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Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capab

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Krasucki, Christopher, and Fran McFarlane. "Electrocardiograms, high-dose antipsychotic treatment and College guidelines." Psychiatric Bulletin 20, no. 6 (1996): 326–30. http://dx.doi.org/10.1192/pb.20.6.326.

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A cross-sectional survey of all in-patients at a psychiatric hospital was performed to see to what extent College guidelines on neuroleptic prescribing were being adhered to. Thirteen (7.3%) patients were found to be on high doses and one (0.6%) on megadoses. Only 23% in the high-dose group had had an ECG at all, and Just 7.7% had had one in the previous three months. The single patient on megadose treatment had had an ECG but not In the previous three months. Prescribing habits that could potentially have led to 21.9% of patients receiving high doses and 9.0% megadoses were identified, and re

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Santos, Paulo, Carlos Martins, Luísa Sá, Alberto Hespanhol, and Luciana Couto. "Motives for requesting an electrocardiogram in primary health care." Ciência & Saúde Coletiva 20, no. 5 (2015): 1549–54. http://dx.doi.org/10.1590/1413-81232015205.10062014.

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The management of requests for diagnostic exams presents its own inherent characteristics in primary health care and reflects the specific nature of the physician-patient relationship. The scope of the study was to identify the reasons for requesting an electrocardiogram (ECG) in primary health care. A cross-sectional study was conducted in an urban region in Portugal, establishing the motives to ask for an ECG consecutively over two years, starting on 01/03/2007 using data retrieved from structured forms filled out by the physician at the moment of requesting the exam. A total of 870 ECGs of

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Crowther, George, Mani S. Krishnan, Jonathan Richardson, Robert Bowes, Andrew Fitzpatrick, and Muzahir H. Tayebjee. "What training should psychiatrists have to interpret six- and 12-lead electrocardiograms?" BJPsych Bulletin, January 26, 2023, 1–5. http://dx.doi.org/10.1192/bjb.2022.87.

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To monitor for drug-related cardiac arrhythmias, psychiatrists regularly perform and interpret 12-lead (12L) and, increasingly often, six-lead (6L) electrocardiograms (ECGs). It is not known how training on this complex skill is updated or how well psychiatrists can interpret relevant arrhythmias on either device. We conducted an online survey and ECG interpretation test of cardiac rhythms relevant to psychiatrists. A total of 183 prescribers took part; 75% did not regularly update their ECG interpretation skills, and only 22% felt confident in interpreting ECGs. Most participants were able to

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Serfözö, Peter Daniel, Robin Sandkühler, Bibiana Blümke, et al. "An Augmented Reality-Based Method to Assess Precordial Electrocardio-gram Leads. A Feasibility Trial." European Heart Journal - Digital Health, July 27, 2023. http://dx.doi.org/10.1093/ehjdh/ztad046.

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Abstract Background It has been demonstrated that several cardiac pathologies including myocardial ischemia can be detected using smartwatch electrocardiograms. Correct placement of bipolar chest leads remains a major challenge in the outpatient population. Methods and results In this feasibility trial, we propose an augmented reality-based smartphone app which guides the user to place the smartwatch to predefined positions of the chest using the front camera of a smartphone. A machine learning model using MobileNet_v2 as backbone was trained to detect the bipolar lead positions V1-V6 and visu

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