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151 results for “Electrocardiogram”
Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects
<p><strong>Study Sample and Ethics Statement</strong></p> <p>The sample consisted of 71 university students, average age 20.38 years (<em>SD</em> = 2.96), 78.8% female. Subjects with previous cardio-vascular disorders and irregular ECG were excluded. The study has been approved by the Institutional Review Board of the Department of Psychology, University of Belgrade No. 2018-19. All participants signed Informed Consents in accordance with the Declaration of Helsinki.</p> <p>In the course of visual examination, it was decided to discard ECG from one subject due to the presence of bigeminial arythmia, so further analysis was performed on 70 subjects instead of 71.</p> <p><strong>Measurement Setup</strong></p> <p>BIOPAC sensors (Biopac Systems Inc., Camino Goleta, CA, USA) were used for recording biosignals in another study (<a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">Bjegojević et al., 2020</a>). Here, we used only ECG signals recorded in sitting relaxed position from standard bipolar Lead I using the BIOPAC MP150 unit with AcqKnowledge software and ECG 100C module with surface H135SG Ag/AgCl electrodes (Kendall/Covidien, Dublin, Ireland). In order to decrease skin-electrode impedance, the skin was cleaned with Nuprep gel (Weaver & Co., Aurora, USA) to reduce skin-electrode impedance. The sampling frequency was set at 2000 Hz and the gain was set to 1000.</p> <p>ECG signals were recorded during relaxation in a sitting position and data were recorded during 2 min long intervals. More information is available in the article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>].</p> <p><strong>Dataset, Code, and Feature Extraction Instructions</strong></p> <ol> <li><a href="https://zenodo.org/record/5736849/files/analysisECG.R?download=1">analysisECG.R</a>, function with analysis procedures written in <a href="https://www.r-project.org/">R programming language</a></li> <li><a href="https://zenodo.org/record/5736849/files/anec12919-sup-0001-supinfo.pdf?download=1">anec12919-sup-0001-supinfo.pdf</a>, detailed ECG processing and feature extraction procedure (also available as <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fanec.12919&file=anec12919-sup-0001-Supinfo.docx">supplementary material</a> for article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>])</li> <li><a href="https://zenodo.org/record/5736849/files/ecg_70.txt?download=1">ecg_70.txt</a>, .txt data file, text format</li> <li><a href="https://zenodo.org/record/5736849/files/mainECG.R?download=1">mainECG.R</a>, a main program written in R programming language</li> <li><a href="https://zenodo.org/record/5736849/files/R-studio-version-info.txt?download=1">R-studio-version-info.txt</a>, the version of <a href="https://www.rstudio.com/">R Studio</a> where the code was tested</li> <li><a href="https://zenodo.org/record/5736849/files/R-version-info.txt?download=1">R-version-info.txt</a><a href="https://zenodo.org/api/files/aa8d999e-1b08-44b5-883f-0540afe8feb8/R-version-info.txt"> </a>, the version of R programming language where the code was tested</li> </ol> <p>For ECG-based feature extraction, we used the following R packages:</p> <ol> <li><strong>signal</strong> - Signal Processing Functions (signal developers (2014). <em>signal: Signal processing</em>. <a href="http://r-forge.r-project.org/projects/signal/">http://r-forge.r-project.org/projects/signal/</a>)</li> <li><strong>pracma</strong> - Practical Numerical Math Functions ( Borchers, H. W. (2019). <em>Package ‘pracma’: Practical numerical math functions</em>. R package version, 2(1). <a href="https://CRAN.R-project.org/package=pracma">https://CRAN.R-project.org/package=pracma</a>)</li> </ol> <p>Please, note that the results of personality trait tests are not available in the current dataset. We are planning to open them in our future research. For more information and planned availability in open access, please, contact the corresponding author of [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>] by e-mail (<a href="mailto:nadica.miljkovic@etf.bg.ac.rs">nadica.miljkovic@etf.bg.ac.rs</a>).</p> <p><strong>Citing Instruction</strong></p> <p>If you find these signals and code useful for your own research or teaching class, please cite relevant dataset and supporting publications:</p> <ol> <li> <p>Boljanić, T., Miljković, N., Lazarević, L. B., Knežević, G., & Milašinović, G. (2021). Relationship between electrocardiogram-based features and personality traits: Machine learning approach. <em>Annals of Noninvasive Electrocardiology</em>, 00, e12919. <a href="https://doi.org/10.1111/anec.12919">https://doi.org/10.1111/anec.12919</a></p> </li> <li> <p>Bjegojević, B., Milosavljević, N., Dubljević, O., Purić, D., & Knežević, G. (2020). <a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">In pursuit of objectivity: Physiological measures as a means of emotion induction procedure validation</a>. <em>XXIVI Scientific Conference on Empirical Studies in Psychology</em>, p. 17-19.</p> </li> <li> <p>Boljanić, T., Miljković, N., Lazarević B. Lj., Knežević, G., & Milašinović, G. (2021). Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects (Version 1) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.5599239">https://doi.org/10.5281/zenodo.5599239</a></p> </li> </ol>
Artificial Intelligence Identifies Individuals with Prediabetes from Single-Lead Electrocardiograms
<h2>Contents</h2> <ul> <li><strong>codes.zip</strong> <ul> <li>For ECG feature extraction (this will need original ECG signal data) <ul> <li>ecg_feature_extraction.sh</li> <li>ecg_feature_extraction.py</li> <li>feature_extractor.py</li> </ul> </li> <li>For training with hyperparameter optimization <ul> <li>train.sh</li> <li>train.py</li> </ul> </li> <li>For prediction of prediabetes/diabetes from ECG feature <ul> <li>test.sh</li> <li>test.py</li> </ul> </li> </ul> </li> <li><strong>raw_ecg_data.zip</strong>: 16,766 ECG records used in our analyses. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 4.88 µV.)</li> <li><strong>external_ecg_data.zip</strong>: 2,456 ECG records used in our external validation. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 1 µV.)</li> <li><strong>participant_characteristics.csv</strong>: Health check records of 16,766 participants where the information below are stored. <ul> <li>participant_id: IDs for participant. Some IDs are duplicated because the dataset contains multiple records from some of the participants.</li> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>age: The age of each participant at the time of the health checkup</li> <li>male_sex: If the participant is male, "True" is recorded</li> <li>smoking: if the participant smokes, "True" is recorded</li> <li>drinking: 1 for "rarely", 2 for "occasionally" and 3 for "regularly" is recorded according to the frequency of drinking</li> <li>height: participant's height in centimeters (cm)</li> <li>weight: participant's body weight in kilograms (kg)</li> <li>BMI: body mass index, calculated using the formula: weight (kg) / [height (m)]^2</li> <li>pulse_rate: pulse rate in pulse per minute (/min) </li> <li>sBP: systolic blood pressure in mmHg</li> <li>dBP: diastolic blood pressure in mmHg</li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>development_data: "True" in records used as development data in our study</li> </ul> </li> <li><strong>external_cohort_characteristics.csv</strong>: Health check records of 2,456 participants where the information below are stored. <ul> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> </ul> </li> <li><strong>ecg_feature_data.zip</strong>: extracted ECG features (unprocessed), for 12-lead and 1-lead ECG <ul> <li>ecg_features_1-lead.csv [Single-lead (lead I) ECG]</li> <li>ecg_features_12-lead.csv [12-lead ECG]</li> <li>ecg_features_12-leads_external_cohort.csv [12-lead ECG of external cohort]</li> </ul> </li> <li><strong>feature_list.zip</strong>: List of ECG features used (to be used for ECG extraction for original data) <ul> <li>feature_list_269_12-lead.csv [269 features for 12-lead ECG analysis]</li> <li>feature_list_28_1-lead.csv   [28 features for single-lead (lead I) analysis]</li> </ul> </li> <li><strong>model_12-lead.zip, model_1-lead.zip</strong>: model trained with our 12-lead or single-lead (lead I) ECG data, and the classification thresholds, used for test<br> <ul> <li>model_fold_1.pkl - model_fold_10.pkl : model for each of 10-fold cross validation</li> <li>average_threshold.pkl : classification threshold, which is the average of 10-fold</li> </ul> </li> </ul> <p>Codes and data for demo are also available in (https://github.com/dkoga4116/diabetes_detector)</p>
IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database
<h2>Abstract</h2><p>Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.</p><h2>Article</h2><p><a href="https://www.nature.com/articles/s41597-023-02621-1">https://www.nature.com/articles/s41597-023-02621-1</a></p><h2>Repository</h2><p><a href="https://github.com/cedricgilon/iridia-af">https://github.com/cedricgilon/iridia-af</a></p><h2>Versions history</h2><ul><li>2023-10-04: v1.0.1 – remove hidden files from .zip archive</li><li>2023-07-26: v1.0.0 – initial release</li></ul><h2>Keywords</h2><p>Paroxysmal Atrial Fibrillation, AF, long-term electrocardiogram, ECG, Holter monitoring, Database, Dataset, IRIDIA, IRIDIA-AF</p>
Figure 22. Example of electrocardiogram
<p>Figure 22 shows our patient’s electrocardiogram (C.B., female, aged 21, clinically healthy,<br> blood pressure 10/7, blood group A2).</p>
Screening of Valvular Heart Disease Using Single-channel Electrocardiogram
ClinicalTrials.gov study NCT07099417. IPD Sharing: NO. Countries: 1. Publications: 2.
Guanfacine Immediate-release Electrocardiogram Results (QTc) Study
ClinicalTrials.gov study NCT00672984. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Diagnostic Accuracy of Electrocardiogram for Acute Coronary Occlusion Resulting in Myocardial Infarction
ClinicalTrials.gov study NCT04022668. IPD Sharing: NO. Countries: 1. Publications: 1.
Volatilome and Single-Lead Electrocardiogram Optimize Ischemic Heart Disease Diagnosis Using Machine Learning Models
ClinicalTrials.gov study NCT06181799. IPD Sharing: NO. Countries: 1. Publications: 8.
Atrial Fibrillation (AF) and Electrocardiogram (EKG) Interpretation Project ECHO
ClinicalTrials.gov study NCT05890274. IPD Sharing: YES. Countries: 1. Publications: 3.
Electrocardiogram (ECG) Guided Peripherally Inserted Central Catheter (PICC) Placement
ClinicalTrials.gov study NCT00742053. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effect of Buprenorphine Delivered by Buprenorphine Transdermal System (BTDS) at Doses up to 80 Micrograms/Hour (mcg/hr) and Naltrexone on Electrocardiogram (ECG) Intervals in Healthy Volunteers
ClinicalTrials.gov study NCT01999114. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study to Assess the Potential Effects of a Single-Dose Administration of Trabectedin on the QT Intervals of the Electrocardiogram
ClinicalTrials.gov study NCT00786838. IPD Sharing: Not stated. Countries: 7. Publications: 1.
Data From: Cardiac arrhythmia prevalence and risk factors in 24-h electrocardiograms of sedentary horses
Open the record for dataset details and reuse information.
24-hour physiological monitoring: Electrocardiogram, interstitial glucose, and ambulatory blood pressure
Open the record for dataset details and reuse information.
Supplementary Material for 'Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review'
<p>This supplementary material accompanies:</p> <p>P. H. Charlton <em>et al.</em>, “Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review,” <em>IEEE Rev. Biomed. Eng.</em>, vol. 11, pp. 2–20, 2018. DOI: <a href="https://doi.org/10.1109/RBME.2017.2763681">10.1109/RBME.2017.2763681</a></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3741462">Version 1</a>: </strong>This is the version originally accompanying the article (albeit with a few filename corrections from the published version).</p> <p><strong><a href="https://doi.org/10.5281/zenodo.3741463">Version 2</a>: </strong>This version was produced on 6th April 2020. The Excel files have been updated to .xlsx versions, and the Matlab script (BRReviewAnalyses.m) has been amended accordingly. In addition, further functionality has been added to the Matlab script (BRReviewAnalyses.m), and a new script has been added which was not previously released in the supplementary material (BRReviewSearchStrategy.m).</p>
Long-term electrocardiogram and wrist-based photoplethysmogram recordings with annotated atrial fibrillation episodes
<p><strong>Rationale</strong></p> <p>Atrial fibrillation (AF) has emerged as a worldwide cardiovascular epidemic affecting more than 33 million individuals worldwide and carrying a 5-fold increased risk of brain stroke and a 3-fold increased risk of heart failure (Hindricks et al. 2021). AF is a progressive disease, with primary paroxysmal episodes being self-terminating; therefore, the success of complication management highly depends on early arrhythmia detection, which often requires long-term AF monitoring (Keach et al. 2015). Unfortunately, existing devices for long-term AF monitoring are either expensive (implantable cardiac monitors) or inconvenient due to skin irritation (Holter monitors, electrocardiogram (ECG) patches). Thus, it is desirable to develop inexpensive technologies ensuring wearing comfort. Recently, biooptical photoplethysmography (PPG) signal has emerged as such technology with immense potential for convenient long-term AF monitoring (Pereira et al. 2020). However, due to the lack of guidelines for arrhythmia interpretation in PPG, simultaneous ECG recording is needed for verification of the episodes detected in PPG. The present dataset contains simultaneously acquired wrist-based PPG and reference ECG signals with annotated AF episodes, and thus, is particularly suitable for use in the development and testing of automatic PPG-based AF detectors.</p> <p><strong>Subjects and data acquisition protocol</strong></p> <p>The dataset contains long-term ECG and PPG signals from 45 patients with suspected AF monitored for 5 to 8 days (306 days in total). Detailed demographic (sex, age, height, weight) and clinical (diagnosed comorbidities, medications) characteristics of the patients are provided in the supplementary file <strong><em>subject_info.xlsx</em></strong>.</p> <p>The acquisition of the PPG and ECG signals was started at Vilnius University Hospital Santaros Klinikos (Vilnius, Lithuania) and continued for a week at the patient’s home. The PPG signal was acquired at a sampling frequency of 100 Hz using a green LED embedded in a wrist-worn device developed at the Biomedical Engineering Institute (Kaunas, Lithuania). The reference ECG signal was acquired at a sampling frequency of 500 Hz using the Bittium Faros™ 180 ECG device together with the Bittium OmegaSnap™ patch electrode (Oulu, Finland). Additionally, triaxial acceleration signals were acquired with both devices at sampling frequencies of 50 and 25 Hz using wrist-worn and reference ECG devices, respectively. The occurrence times of QRS-complexes in ECG signals were obtained using an open-source QRS detector (Moeyersons et al. 2020), and initial AF episodes were automatically detected using a low-complexity AF detector relying on rhythm irregularity information (Petrėnas et al. 2015). Then, the AF detector output was visually inspected and manually corrected by medical specialists experienced in arrhythmia diagnosis with the aim to find undetected and discard falsely detected episodes.</p> <p>The data acquisition protocol was in accordance with the ethical principles of the Declaration of Helsinki and was approved by Vilnius Region Biomedical Research Ethics Committee (No. 158200-18/7-1052-557). All patients gave written informed consent to participate.</p> <p><strong>Technical details</strong></p> <p>The acquired signals are provided in MAT-files named as follows:</p> <p><strong>XX_YYY</strong>,</p> <p>where <strong>XX</strong> is the patient ID, <strong>YYY</strong> is ECG for the signals from the Bittium Faros™ 180 ECG device and PPG for the signals from the wrist-worn device. For each patient, there are a single continuous ECG recording and multiple PPG recordings because the acquisition of the PPG could be interrupted for a short time due to technical reasons or for a longer time to allow battery charging of the wrist-worn device.</p> <p>In addition to PPG, ECG, and acceleration signals, each file contains a signal header, the day when the recording started with respect to the first monitoring day of the patient, and the time of day when the recording started. The ECG files also contain QRS time indices and calculated RR intervals together with AF annotations on a beat-to-beat basis. PPG and acceleration signals from a wrist-worn device were synchronized to correspond time of ECG acquisition device. </p> <p>In the <strong><em>subject_info.xlsx</em></strong> file, physical inactivity is defined as < 5000 steps/day or < 150 min/week of moderate-intensity exercise or < 75 min/week of high-intensity exercise, excessive physical activity is defined as > 750 min/week of moderate-intensity exercise, and hypertension is classified into stages based on systolic/diastolic blood pressure: stage I corresponding to 140/90–159/99 mmHg, stage II to 160/100–179/109 mmHg, and stage III to ≥ 180/100 mmHg.</p> <p><strong>Limitations</strong></p> <p>When using the resource, researchers should be aware that the PPG and ECG acquisition devices have been synchronized, however, the exact alignment of the signals cannot be reached due to physiological features, e.g., the heart rate obtained from the PPG and ECG signals. Users should also be aware that the sampling frequencies of the devices can vary slightly.</p>
Hypoglycemia-induced electrocardiogram changes in a rodent model of type 1 diabetes
<p>Retrospective data was obtained from studies conducted by Reno et al. Adult male Sprague-Dawley rats (weight, 250–300 g; Charles River Laboratories, Malvern, PA) were housed individually in temperature- and light-controlled environments and fed ad libitum a standard chow diet and water. Rats underwent surgery for carotid artery and jugular vein cannulation as previously described. ECG leads were also placed subcutaneously during surgery. One wire was placed over the lower left rib cage and one was placed over the right supraclavicular fossa. A reference wire was placed over the back. Two days after cannulation, one set of rats received intraperitoneal injections of streptozotocin (65 mg/kg; Sigma, St. Louis, MO) to induce diabetes (n = 37). A second group of rats received sodium citrate buffer and acted as a control, non-diabetic group (n = 54). Blood glucose was measured from the tail vein with a glucometer (Ascensia Contour; Bayer HealthCare, Mishawaka, IN) to ensure diabetes. Rats were diabetic for two weeks prior to the hypoglycemic clamp. Overnight fasted, awake, unrestrained non-diabetic and diabetic rats underwent hyperinsulinemic (0.2 units ⋅ kg−1 ⋅ min−1; Humulin R), severe hypoglycemic (10–15 mg/dL) single step clamps with continuous ECGs for 3 hours, as previously described ECGs were recorded every millisecond (ms) using PowerLab 26T software (LabChart; ADInstruments, Colorado Springs, CO). Arterial blood was used to determine glucose levels during the clamp with a glucometer (Ascensia Contour BG monitors; Bayer Healthcare Mishawaka, IN) every 15-minutes.</p> <p> </p> <p>Reno CM, Daphna-Iken D, Chen YS, VanderWeele J, Jethi K, Fisher SJ. Severe hypoglycemia-induced lethal cardiac arrhythmias are mediated by sympathoadrenal activation. Diabetes. 2013;62(10):3570-81.</p> <p> </p> <p>Reno CM, VanderWeele J, Bayles J, Litvin M, Skinner A, Jordan A, et al. Severe Hypoglycemia-Induced Fatal Cardiac Arrhythmias Are Augmented by Diabetes and Attenuated by Recurrent Hypoglycemia. Diabetes. 2017;66(12):3091-7.</p> <p> </p> <p> </p> <p> </p>
Electrocardiogram-based Artificial Intelligence-assisted Detection of Heart Disease
ClinicalTrials.gov study NCT05442203. IPD Sharing: NO. Countries: 1. Publications: 33.
Home Electrocardiogram (ECG) Monitoring After Heart Transplantation
ClinicalTrials.gov study NCT01365806. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of the Electrocardiogram Effects and Pharmacokinetics of Ferumoxytol in Healthy Men and Women
ClinicalTrials.gov study NCT00360425. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.