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Electrocardiography

Electrocardiography is a non-invasive diagnostic technique used to record the electrical activity of the heart.
It provides valuable insights into cardiac function, heart rate, rhythm, and potential underlying conditions.
The electrocardiogram (ECG) captures the electrical signals generated by the heart's muscle contractions, allowing healthcare professionals to identify abnormalities and monitor cardiac health.
Electrocardiography plays a crucial role in the diagnosis and management of a wide range of cardiovascular disorders, including arrhythmias, myocardial infarctions, and conduction disturbances.
This powerful tool enables early detection, timely intervention, and improved patient outcomes.
Leveraging the latest advancements in technology, electrocardiography continues to evolve, offering enhanced accuracy, portability, and accessibility for healthcare providers and patients alike.
Wheter you are a researcher, clinician, or individual interested in heart health, understanding the principles and applications of electrocardiography is essential for optimizing cardiovascular care.

Most cited protocols related to «Electrocardiography»

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Publication 2020
Activated Partial Thromboplastin Time Axilla Bacteremia Blood Blood Coagulation Disorders Bronchoalveolar Lavage Fluid Chinese Congenital Abnormality COVID 19 Echocardiography Electrocardiography Fever Heart Heart Injuries Hospital Administration Hypersensitivity Hypoproteinemia Kidney Injury, Acute pathogenesis Patients Pneumonia Pneumonia, Ventilator-Associated Respiratory Distress Syndrome, Acute Respiratory System Seafood Secondary Infections Septicemia Septic Shock Serum Albumin Sputum Times, Prothrombin Troponin I

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Publication 2020
Bacteremia Blood Bronchoalveolar Lavage Fluid Congenital Abnormality Creatinine Echocardiography Electrocardiography Heart Heart Injuries Influenza in Birds Inhalation Kidney Diseases Kidney Injury, Acute Oxygen pathogenesis Patients Pneumonia, Hospital Acquired Respiratory Distress Syndrome, Acute Respiratory System SARS-CoV-2 Secondary Infections Serum Shock Sputum Troponin I Urine
In the LifeLines Cohort Study, a recruitment strategy was adopted that aimed to include three generations of participants. Firstly, all GPs in the three northern provinces of the Netherlands were invited to participate and asked to invite their registered patients aged 25–49 years. Patients who were unable to read Dutch or who had limited life expectancy due to severe illness were excluded by the GP and not invited for participation. Participants who gave written informed consent were included as the “index population”. Subsequently, all persons in the index population were asked to indicate whether family members (partner, parents, parents-in-law, and children) could be invited and to provide their contact details. Family members were invited by LifeLines; those who gave their informed consent were included in the study as “family member”. Furthermore, persons aged 18 years and older could participate in this study through “self-registration” via the LifeLines website. These self-registrants were also asked to invite family members as outlined above. LifeLines aimed to include three generations of participants, but individuals who had no family member participating in the study were not excluded. Although the inclusion started in 2006 and ended in 2013, most participants (57%) were included in the last two years.
All participants aged 18 years and older were asked to complete a comprehensive questionnaire covering the occurrence of diseases, general health, lifestyle, diet, physical activity, personality, social support, medication use and more. In addition, all participants aged 18 years and older were invited to one of the 10 research sites within the region where a number of measurements were performed covering anthropometry, blood pressure, pulmonary function, heart function (electrocardiogram) and cognition [2 ,9 ]. In addition, a fasting blood sample was taken, 24 hour urine was collected, and psychiatric disorders were assessed in an interview with one of the research nurses [2 ,10 (link)].
All participants signed an informed consent form before they received an invitation for the physical examination. The LifeLines Cohort Study is conducted according to the principles of the Declaration of Helsinki and in accordance with research code University Medical Center Groningen (UMCG). The LifeLines study is approved by the medical ethical committee of the UMCG, the Netherlands. For a comprehensive overview of the data collection, please visit the LifeLines catalogue at www.LifeLines.net.
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Publication 2015
BLOOD Blood Pressure Child Cognition Diet Electrocardiography Family Member Heart Lung Nurses Parent Patients Pharmaceutical Preparations Physical Examination Problem Behavior Urine
Means and standard deviation and frequency distribution of relevant covariates were calculated by cohort and race. We initially ran cohort‐ and race‐specific Cox proportional hazard models to assess individual predictors of AF after age‐ and sex‐adjustment in each cohort up to 7 years of follow‐up. Variables considered included age, sex, height, weight, current smoking, systolic and diastolic blood pressure, use of antihypertensive medication, history of diabetes, fasting blood glucose, estimated glomerular filtration rate (eGFR) <60 mL/kg per m2,20 (link) total blood cholesterol, HDL cholesterol, triglycerides, heart rate, electrocardiographic‐derived left ventricular hypertrophy, PR interval, history of coronary artery bypass graft (CABG), history of heart failure, history of myocardial infarction, and history of stroke. We selected as candidate predictors for our pooled model any variable significantly associated with AF (P<0.05) in at least 2 of the 3 cohorts, and ran the final Cox proportional hazards model on our participant‐specific pooled data using backward selection of variables (P<0.05 to remain in the model). Age, sex, and race interactions were tested, as was the assumption of proportional hazards. Model‐based individual 5‐year risk of AF was calculated. We evaluated model performance using the C‐statistic,21 (link) discrimination slopes,22 (link) and Nam and D'Agostino's modified Hosmer‐Lemeshow chi‐square statistic for survival analysis.23 To facilitate the use of our score in those clinical settings with limited access to electrocardiograms or blood tests, we first developed a predictive model that did not require information from electrocardiogram and blood tests (which we labeled “simple model”). We then developed a more complex model adding electrocardiographic variables and blood tests (labeled “augmented model”). Variables were retained in the models if they were significantly associated with AF incidence (P<0.05). We calculated the added predicted value of the augmented model versus the simple model with the increment in the C‐statistic and the categorical net reclassification improvement (NRI) using the following risk categories: <2.5%, 2.5% to 5%, >5%.22 (link)
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Publication 2013
Antihypertensive Agents BLOOD Blood Glucose Cerebrovascular Accident Cholesterol Congestive Heart Failure Coronary Artery Bypass Surgery Diabetes Mellitus Discrimination, Psychology Electrocardiogram Electrocardiography Glomerular Filtration Rate Hematologic Tests High Density Lipoprotein Cholesterol Left Ventricular Hypertrophy Myocardial Infarction Pressure, Diastolic Rate, Heart Systole Triglycerides
We conducted a genetic association study with three stages as displayed in Figure 1. Stage 1 consisted of the Myocardial Infarction Genetics Consortium (MIGen), a collection of 2,967 cases of early-onset MI (in men ≤50 years old or women ≤60 years old) and 3,075 age- and sex-matched controls free of MI from six international sites: Boston and Seattle in the United States as well as Sweden, Finland, Spain, and Italy (Table 1). At each site, MI was diagnosed on the basis of autopsy evidence of fatal MI or a combination of chest pain, electrocardiographic evidence of MI, or elevation of one or more cardiac biomarkers (creatine kinase or cardiac troponin). The mean age at the time of MI was 41 years among male cases and 47 years among female cases.
We took forward SNPs into two stages of replication (Stages 2 and 3, Figure 1). 1441 SNPs were tested in Stage 2 based on two criteria: i) strength of statistical evidence in Stage 1 (1433 SNPs from loci with P < 10-3 in Stage 1) or ii) belonging to one of eight reported loci from recent genome-wide association studies for coronary artery disease (a common SNP from each of 9p21.3, near CXCL12, SMAD3, MTHFD1L, MIA3, near CELSR2/PSRC1/SORT1, 2q36, and PCSK9)3 (link),7 (link).
Stage 2 consisted of in silico comparisons with four recently completed GWAS for MI consisting of a symmetric effective sample size of up to 3,942 cases of MI and 3,942 controls. These studies included the Wellcome Trust Case Control Consortium Coronary Heart Disease study3 (link), German MI Family Study I3 (link), PennCATH, and MedStar (Supplementary Table 1). In each Stage 2 study, the analysis was restricted to the phenotype of MI with an age of onset threshold of <66 years for men or women. Although this age cutoff is slightly less restrictive than that used in Stage 1, this cutoff is at or below the mean age of first MI in the US (65 years for men and 70 years for women).
Thirty-three SNPs were taken forward to Stage 3, which consisted of genotyping an additional 6 studies with a symmetric effective sample size of up to 5,469 cases of MI and 5,469 controls. These six studies included Acute MI Gene Study/Dortmund Health Study, Verona Heart Study29 (link), Mid-America Heart Institute Study30 (link), Irish Family Study31 (link), German MI Family Study II, and INTERHEART32 (link) (European ancestry and South Asian ancestry each analyzed separately) (Supplementary Table 2). Stage 3 was comprised of 25 SNPs with the best combined statistical evidence for MI from Stages 1 and 2 (P < 10-5) and the eight previously-reported SNPs discussed above. In each Stage 3 study, the analysis was restricted to the phenotype of MI and in four of the six studies, an age of onset threshold was established at <66 years for men or women.
Publication 2009
Asian Persons Autopsy Biological Markers CELSR2 protein, human Chemokine CXCL12 Chest Pain Coronary Artery Disease Creatine Kinase DNA Replication Electrocardiography Europeans Genes Genetic Association Studies Genome-Wide Association Study Heart Heart Disease, Coronary Males migen Myocardial Infarction PCSK9 protein, human Phenotype Single Nucleotide Polymorphism SMAD3 protein, human SORT1 protein, human Troponin Woman

Most recents protocols related to «Electrocardiography»

Example 5

In patients with congestive heart failure, introducing number(s)/factor(s) generated based on the heart rate variability into a treatment regimen, such as the ratio between two consecutive heartbeat variabilities, may improve the response to diuretic therapy. If the patient already experienced loss of the heart rate variability, introducing the change in therapy can be followed by monitoring the effect of treatment on reversal of this loss. A use of previous electrocardiogram strips from the subject may enable to generate a “healthier-pattern of variability”. Based on the patient's variability pattern the app determines a dynamic individualized irregularity pattern for a dosing regimen.

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Patent 2024
Biotin Congestive Heart Failure Diuretics Electrocardiography Medical Devices Patients Pharmaceutical Preparations Pulse Rate Rate, Heart Treatment Protocols
The following data were recorded during the preoperative examination: Sex, age, height, body weight, BMI, smoking history, complete blood count (leukocytes, hemoglobin, platelets), liver function tests (liver enzymes, albumin), renal function tests, preoperative oxygen saturation, history of previous surgery, and concomitant diseases (type 2 diabetes, hypertension, pulmonary and cardiac diseases).
The following data were also collected: History and physical examination findings, chest radiographs, computed tomographic examinations of the chest (CT), electrocardiography (ECG) and echocardiography (if required), pulmonary function test results (forced expiratory volume (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio), and arterial blood gases. In patients with lung cancer, the type and stage of malignancy were determined, and flexible bronchoscopy was performed.
During the intraoperative process, the type of endotracheal tube, the duration of anesthesia and surgery, the surgical procedure (VATS, thoracotomy, mediastinoscopy, and others) performed, and complications that required intraoperative treatment were also noted.
PPCs have been defined as complications that occur in the postoperative period and cause clinical conditions.
Publication 2023
Albumins Anesthesia Arteries Blood Gas Analysis Blood Platelets Body Weight Bronchoscopy Chest Complete Blood Count concomitant disease Diabetes Mellitus, Non-Insulin-Dependent Echocardiography Electrocardiography Enzymes Exhaling Forced Vital Capacity Heart Diseases Hemoglobin High Blood Pressures Kidney Function Tests Leukocytes Liver Liver Function Tests Lung Lung Cancer Mediastinoscopy Operative Surgical Procedures Oxygen Saturation Patients Physical Examination Radiography, Thoracic Staging, Cancer Tests, Pulmonary Function Thoracic Surgery, Video-Assisted Thoracotomy Training Programs Volumes, Forced Expiratory X-Ray Computed Tomography
Clinical notes include any unstructured text information such as radiography reports, electrocardiogram reports, discharge summaries, admission notes, and daily notes made by the care team.
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Publication 2023
Electrocardiography Patient Discharge X-Rays, Diagnostic
Healthy white male participants were eligible for inclusion to the study if they were ≥ 40 years of age and ≤ 60 years of age and had a body mass index ≥ 18.0 kg/m2 and ≤ 29.9 kg/m2. Participants were excluded if they had a clinically relevant finding on the electrocardiogram (ECG), had systolic blood pressure (SBP) < 100 mmHg or > 145 mmHg, had diastolic blood pressure (DBP) < 60 mmHg or > 95 mmHg, or heart rate (HR) < 50 or > 95 beats per min (bpm). Regular use of medication within 4 weeks and any use of medication, herbal products, or vitamins within 14 days prior to the first study drug administration were prohibited. Regular daily consumption of more than ten cigarettes and intake of foods and beverages containing grapefruit within 14 days before study drug administration were exclusion criteria. A full list of inclusion and exclusion criteria is included in Table S1 of the Electronic Supplementary Material (ESM).
Publication 2023
Beverages Citrus paradisi Eating Electrocardiography Healthy Volunteers Index, Body Mass Males Pharmaceutical Preparations Pressure, Diastolic Rate, Heart Systolic Pressure Vitamins
HRV, the fluctuation of heartbeat intervals measured using an electrocardiogram, is used to evaluate autonomic nerve activities [26 (link), 27 (link)]. HRV tends to be lower in a person with anxiety or depression. However, it is relative rather than absolute; therefore, it is not directly compared among individuals.
Standard deviation of the normal- to- normal interval (SDNN) is the quantification of HRV to further compare it among individuals. Particularly, SDNN is the standard deviation of the R-R intervals of the heartbeat in a certain time duration and is obtained via time-domain analysis. SDNN was used to evaluate cardiovascular compatibility. SDNN includes all the different types of variations and represents total variability [28 (link)]. It assesses the flexibility of the autonomic nervous system and the balance of sympathetic and parasympathetic nervous systems, with an increase in SDNN reflecting the stability of these systems [16 (link), 29 (link)]. The grand mean of SDNN scores among resting adults is 50 mseconds [29 (link)].
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Publication 2023
Adult Anxiety Autonomic Nerve Cardiovascular System Electrocardiography Nervous System, Autonomic Parasympathetic Nervous System Pulse Rate

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