The Canadian Heart Health Data Base (CHHDB) is a compilation of data from ten Provincial Heart Health Surveys conducted between 1986 and 1992. The Provincial Heart Health Surveys were carried out as part of the Canadian Heart Health Initiative and have been a collaborative effort among the provincial departments of health, Health Canada, The Heart and Stroke Foundation of Canada, and provincial heart and stroke foundations.

The CHHDB consists of two data sets. The first is the Heart Health Dataset, consisting of a survey of core information from 23,129 entries collected by all Provincial Heart Health Surveys between 1986 and 1992. The core information has data on major biological risk factors (blood pressure and blood lipids) and information on knowledge and awareness of causes and consequences of cardiovascular diseases and associated risk factors.

The second is the Family History Dataset, consisting of information on demographic information and health history collected by four provinces (Quebec, Ontario, Saskatchewan and Alberta) from 9,286 respondents between 1989 and 1992.

ECG images dataset of Cardiac Patients created under the auspices of Ch. Pervaiz Elahi Institute of Cardiology Multan, Pakistan that aims to help the scientific community for conducting the research for Cardiovascular diseases.

This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.This program establishes a deep learning model of CNN+LSTM, which is used for continuous monitoring of exercise heart rate with PPG signals containing motion artifacts, and has achieved good results in the PPG-DaLiA database. The description is as follows: 1. The file main_program_file is the main file, including model construction, data processing, data training, model data verification, and other processing programs for PPG signals that are not used in this article. model: build exercise heart rate monitoring model file; activity_time.xls: Collect each activity time node of each volunteer signal obtained from the PPG-DaLiA database; original_data_read.py: signal data preprocessing program (signal from the PPG-DaLiA database); ppg_filed_hr_cornet_estimate.py: training and prediction program for all volunteers’ PPG signals; ppg_filed_hr_cornet_estimate_single.py: a program to predict the PPG signal of a single volunteer; _1d_cnn, _2d_cnn, ppg_excerise_cnn_type.py, ppg_filed_hr_cnn_estimate.py: programs that use the CNN method for prediction; spc_hr_cornet_estimate.py, spc_hr_cnn_estimate.py: programs for predicting and verifying using other database PPG signals. save_model_estimate_hr.py, save_model_estimate_hr_spc.py: save the heart rate prediction model and the model program for the heart rate prediction model to be used in the SPC database. out_fig: model prediction picture output folder; 2. Data source The data comes from the PPG-DaLiA database (PPG Data For Daily Life Activity, https://archive.ics.uci.edu/ml/datasets/PPG-DaLiA): The database comes from Robert Bosch GmbH and Bosch Sensortec GmbH. The signals in this database come from 15 volunteers of different ages and different physical conditions. PPG and heart rate data are continuously collected during different exercises. The preprocessing of the downloaded data is in the program original_data_read.py. 3.other _0_basic_fun, ch3_preprocess, my_pyhht_lib: some external references of the main program, mainly the functions called by the data preprocessing part, and the main program can view their functions.

Context This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer-valued from 0 (no presence) to 4.

Acknowledgements Creators:

Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., PhD. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Inspiration Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).

See if you can find any other trends in heart data to predict certain cardiovascular events or find any clear indications of heart health.

The sample dataset was selected from Isfahan Cardiovascular Research Center (ICRC). It was de-identified due to the restrictions on individual-level data (Personal Health Information Protection Act) based on [1,2]. They could be used to identify 10-year CVD incidence risk based on the model proposed in the corresponding publication [3]. The representative age category could be used in the model instead of the exact age that had been removed since it was potentially identifying as the recruitment period was short and was fully described [1]. Since the Hazard Ratio of age was 1.038, the estimated risks are comparable to what was obtained with the exact age. The categories of Systolic Blood Pressure (SBP) and Total Cholesterol (Tch) were used in the model, and thus here provided. High waist-to-hip ratio (WHR) was defined as WHR ≥ 0.80 in women and ≥ 0.95 in men. 1. Hrynaszkiewicz I, Norton ML, Vickers AJ, Altman DG. Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers. BMJ. 2010;340, http://www.bmj.com/content/340/bmj.c181.long .2. National Heart, Lung and Blood Institute. Guidelines for Preparing Clinical Study Data Sets for Submission to the NHLBI Data Repository. https://www.nhlbi.nih.gov/research/funding/human-subjects/set-preparation-guidelines . 3.
Nizal Sarrafzadegan, Razieh Hassannejad, Hamid Reza Marateb, Mohammad Talaei, Masoumeh Sadeghi, Hamid Reza Roohafza, Farzad Masoudkabir, Shahram OveisGharan, Marjan Mansourian, Mohammad Reza Mohebian, Miquel Angel Mañanas, "PARS Risk Charts: A 10-year Study of Risk Assessment for Cardiovascular Diseases in Eastern Mediterranean Region", submitted to PLOS One.

Finding a good data source is the first step toward creating a database. Cardiovascular illnesses (CVDs) are the major cause of death worldwide. CVDs include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart and blood vessel problems. According to the World Health Organization, 17.9 million people die each year. Heart attacks and strokes account for more than four out of every five CVD deaths, with one-third of these deaths occurring before the age of 70. A comprehensive database for factors that contribute to a heart attack has been constructed. The main purpose here is to collect characteristics of Heart Attack or factors that contribute to it. The size of the dataset is 1319 samples, which have nine fields, where eight fields are for input fields and one field for an output field. Age, gender, heart rate (impulse), systolic BP (pressurehight), diastolic BP (pressurelow), blood sugar(glucose), CK-MB (kcm), and Test-Troponin (troponin) are representing the input fields, while the output field pertains to the presence of heart attack (class), which is divided into two categories (negative and positive); negative refers to the absence of a heart attack, while positive refers to the presence of a heart attack.

NOTE: This dataset is Inactive and is no longer supported. Any historical knowledge regarding meta data or it's creation is no longer available. All known information is proved as part of this data set. The National Cardiac Device Surveillance Program Database supports the Eastern Pacemaker Surveillance Center (EPSC) staff in its function of monitoring some 11,000 Veterans Health Administration (VHA) patients who have implanted pacemakers or cardioverters. The database stores medically useful information about the patients and their pacemaker test results in order to highlight serial changes, which determine whether the pacemaker is still functioning normally, or whether the patient requires further intervention. The EPSC staff performs regular telephonic checkups, in conjunction with less frequent in-hospital clinic checkups, to determine when pacemakers need to be replaced. Patients are scheduled and called by the Pacemaker Surveillance Center, and have their electrocardiogram recorded and analyzed over the phone, using wires attached to their fingers and a VHA-supplied transmitter. Additionally, some patients are monitored via web-based downloads of their device telemetry. The Pacemaker Center also provides in-hospital clinic checkups for local Washington DC VHA pacemaker patients. All information obtained during the checkups is recorded in the EPSC Database. The database also contains records of pacemaker patients being monitored by VHA facilities east of the Mississippi and who are not being monitored directly by their respective VA medical centers. The VHA Department of Medical Services encourages local VHA medical centers to refer their patients for pacemaker follow-up monitoring to either the Eastern Surveillance Center or to the counterpart Western Surveillance Center in San Francisco, whichever is geographically appropriate. However, referral is optional. The database also maintains a registry of all VHA patients, living and deceased, who have had pacemakers implanted at, or who have been monitored by, VHA facilities. The EPSC receives information for the registry directly from the medical centers for patients that it does not monitor, totaling over 80,000 as of 2010.

The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.

Interpretation of Electrocardiograms (ECG) is increasingly complemented by algorithms. These algorithms are based on large datasets. This ECG database consists of children and adults with congenital heart defects (CHD) including many arrhythmia annotations. This dataset, notable for its manual annotations and inclusion of intracardiac electrograms alongside traditional 12-lead ECGs, offers 1075.85 minutes of recordings (in total 113924 annotated beats) that capture various cardiac rhythms and arrhythmias such as supraventricular tachycardia and ventricular tachycardia. The data were meticulously collected from patients undergoing electrophysiological studies, with subsequent annotations by expert reviewers using the LightWAVE® software. The significance of this database lies in its focus on pediatric arrhythmias and arrhythmias of patients with congenital heart defect areas currently underrepresented in existing datasets, which predominantly feature adult pathologies. This resource aims to enhance algorithmic development for ECG interpretation, leveraging machine learning to improve diagnosis and treatment outcomes in these sensitive groups. The dataset not only serves as a critical tool for developing precision medicine but also sets a precedent for future expansions to include a broader spectrum of congenital heart defects conditions, thereby supporting the evolution of cardiac care through advanced computational techniques.

Sunnybrook Cardiac Data

The Sunnybrook Cardiac Data (SCD), also known as the 2009 Cardiac MR Left Ventricle Segmentation Challenge data, consist of 45 cine-MRI images from a mixed of patients and pathologies: healthy, hypertrophy, heart failure with infarction and heart failure without infarction. Subset of this data set was first used in the automated myocardium segmentation challenge from short-axis MRI, held by a MICCAI workshop in 2009. The whole complete data set is now available in the CAP database with public domain license. Classification There are four pathological groups in this data set, which were classified based on (K Alfakih et al., JMRI 2003) paper, i.e.: 1. Heart failure with infarction (HF-I) group had ejection fraction (EF) < 40% and evidence of late gadolinium (Gd) enhancement. 2. Heart failure without infarction (HF) group had EF < 40% and no late Gd enhancement. 3. LV hypertrophy (HYP) group had normal EF (> 55%) and a ratio of left ventricular (LV) mass over body surface area is > 83 g/m2. 4. Healthy (N) group had EF > 55% and no hypertrophy. The following table shows group statistics written as average (stddev) :

N (n=9)

HYP (n=12)

HF (n=12)

HF-I (n=12)

End Diastolic Volume (ml)

115.69 (36.89)

114.39 (50.46)

233.67 (63.21)

244.92 (86.02)

End Systolic Volume (ml)

43.10 (14.74)

43.11 (24.50)

158.28 (56.34)

174.34 (90.64)

Ejection Fraction (%)

62.93 (3.65)

62.72 (9.22)

33.09 (13.07)

32.01 (12.27)

Left Ventricular Mass (g)

130.27 (32.69)

175.87 (85.70)

193.69 (39.01)

201.32 (45.24) Availability The Cardiac Atlas Project provides the dissemination of the Sunnybrook data by hosting them in the CAP databases. Finite element models (see supporting files section below) derived from these data are also provided. The whole complete data are available for any users, including the guest user account. License and attribution of these data set, including its derivatives, follows the Public Domain (CC0 1.0 Universal). If you are using this data in a publication, please cite the following reference: Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. “Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.” The MIDAS Journal – Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070 Data contributor 1. Perry Radau – Sunnybrook Health Sciences Centre, Toronto, Canada. More information · The original 2009 LV Segmentation Challenge webpage. · Promotional poster for the 2009 LV Segmentation Challenge. · The challenge results published in the MIDAS journal.

ObjectiveAccumulating evidence from other countries indicates potential associations between gout and cardiovascular diseases; however, the associations of gout with cardiovascular diseases, particularly stroke, ischemic heart disease, and heart failure, remain ambiguous in the Korean population. We hypothesized that individuals with gout are at a higher likelihood of stroke, ischemic heart disease, or heart failure. This study expands upon previous research by ensuring a comparable baseline between patient and control groups and analyzing 16 years of data derived from an extensive healthcare database.MethodsWe selected 22,480 patients with gout and 22,480 control individuals from the Korean National Health Insurance Service-Health Screening Cohort database (2002–2019), and matched them at a 1:1 ratio according to sex, age, income, and residence. A Cox proportional hazard model with weighted overlap was employed to examine the relationship between gout and the risk of stroke, ischemic heart disease, or heart failure after adjustment for several covariates.ResultsThe incidences of stroke, ischemic heart disease, or heart failure in participants with gout were slightly higher than those in controls (stroke: 9.84 vs. 8.41 per 1000 person-years; ischemic heart disease: 9.77 vs. 7.15 per 1000 person-years; heart failure: 2.47 vs. 1.46 per 1000 person-years). After adjustment, the gout group had an 11% (95% confidence interval [CI] = 1.04–1.19), 28% (95% CI = 1.19–1.37), or 64% (95% CI = 1.41–1.91) higher likelihood of experiencing stroke, ischemic heart disease, or heart failure, respectively, than the control group.ConclusionThe present findings suggest that individuals with gout in the Korean population, particularly those aged ≥ 60 years, were more likely to have stroke, ischemic heart disease, or heart failure.

Rationale According to Eurostat [1], timely interventions could prevent two-thirds of deaths in individuals under 75, with myocardial infarction being the leading cause [2]. Myocardial infarction is typically diagnosed with a 12-lead electrocardiogram (ECG), often unavailable outside the hospital when early symptoms like chest pain occur. Accessible technology for home-based multilead ECG acquisition, followed by automatic ECG analysis or specialist review, may be a promising solution to this problem. Several smartwatches on the market offer ECG functionality; however, they cannot acquire chest ECG leads, which is a crucial drawback, as anterior, septal, and lateral infarctions cannot be diagnosed without such leads. To address the lack of ECG leads, a wrist-worn wearable device, featuring three electrodes, can be used to enable the simultaneous acquisition of two ECG leads with a single touch (hereafter referred to as a wECG) [3]. One lead is standard lead I, while the other is acquired from chest location, thereby enhancing the amount of cardiac information. However, since the latter lead is non-standard, it poses challenges for interpretation. Therefore, adopting the standard ECG configuration, e.g., through 12-lead ECG synthesis, is essential to present the information in a format that is clinically interpretable. The wECGdb database contains ECGs simultaneously acquired using a wrist-worn device and a 12-lead ECG for reference. Therefore, it is particularly suitable for the development and testing algorithms for 12-lead ECG synthesis from wECG. Subjects The database consists of data from 92 participants, divided into three groups: healthy participants, patients with acute myocardial infarction, and patients with other cardiovascular disease (CVD). To be eligible for inclusion, participants had to be at least 18 years old, without an implanted cardiac device, and without cognitive or linguistic impairments. The acute myocardial infarction group consisted of patients diagnosed with either STEMI or NSTEMI, with ECGs taken within 24 hours of percutaneous coronary intervention. The other CVD group included patients with heart conditions that caused infarction-like changes in the ECG. The healthy group consisted of individuals with no history of heart disease. The patients were recruited from the inpatient wards of the Cardiology Department at Vilnius University Hospital Santaros Klinikos, Lithuania. All eligible participants provided signed, written informed consent in accordance with the ethical principles outlined in the Declaration of Helsinki. The study was approved by the regional bioethics committee, under reference number 158200-18/7-1052-557. wECG acquisition The wrist-worn wearable device, developed at the Biomedical Engineering Institute of Kaunas University of Technology [3, 4], equipped with three bio-potential electrodes, was used to acquire two wECG leads at a single touch. The wECG was acquired at a sampling rate of 500 Hz. For all participants, the device was positioned slightly above the wrist on the left arm. Lead I, between the left arm (LA) and right arm (RA), was acquired by touching one electrode with the right index finger. The other lead was obtained by placing the electrode on the strap against a specific part of the body. Acquisition of a standard 12-lead ECG The standard 12-lead ECG was acquired using disposable Ag/AgCl electrodes at 200 Hz with the Euroholter 12view recorder (Lumed, Italy) and resampled to 500 Hz to match the sampling rate of the wECG. ECG preprocessing Both the wECG and the 12-lead ECG were filtered using a high-pass Butterworth filter with a cutoff frequency of 0.5 Hz and a low-pass Parks-McClellan filter with a cutoff frequency of 100 Hz. Some elderly patients had difficulty maintaining consistent pressure on the electrode, resulting in fewer high-quality wECGs. The database includes only wECGs with acceptable signal quality, as assessed by the consensus beat detection signal quality index [5]. To improve wECG quality, each beat in each lead was replaced by an amplitude-scaled average beat. Technical details The wECGdb database is divided into development and test datasets. The development dataset was collected by asking participants to touch specific body sites under clinician guidance: an abdominal site (A), located 2 cm to the left of the umbilicus, and two precordial sites corresponding to the conventional V3 and V5 electrode sites, positioned just below those used to acquire the standard 12-lead ECG. This protocol produced three leads: LA-A, LA-V3, and LA-V5. The test dataset includes only the LA-A lead, which was self-acquired by participants without clinician assistance. Each recording lasted approximately one minute, with at least a one-minute interval between recordings. The acquired signals are provided in MAT-files labeled as follows: dataset_XXX, where XXX represents the participant ID. Each dataset_XXX file contains five struc...

This report contains generic information about pacing and ICD practice in the United Kingdom and Republic of Ireland up to and including 2002.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A specialized database for mouse heart and calcium signaling toolkit genes. It contains the functional gene modules pre-calculated from the microarray data compendium using various algorithms for genetic network analyses. The Heart and Calcium functional Network (HCNet) database is a collection of functional gene clusters calculated from microarray data compendium obtained from the Korea Systems Biology Initiative and from the publicly available GEO database. It was designed to assist experimentalists especially in the field of cardiac and calcium signaling research to detect potential network motifs and gene clusters that are functionally related or co-regulated by common transcription factors. Genes of defined numbers are classified into two categories, 1) heart-specific genes and 2) heart-specific genes plus calcium signaling toolkit-genes.

This notebook will introduce some foundation machine learning and data science concepts by exploring the problem of heart disease classification.

The original data came from the Cleveland database from UCI Machine Learning Repository.

The original database contains 76 attributes, but here only 14 attributes will be used. Attributes (also called features) are the variables that we'll use to predict our target

Each of the 28 waveform files consists of 12 arrays, representing the twelve leads. The ECGs were obtained using a General Electric (GE) MAC VUE 360 electrocardiograph and interpreted using the built-in ECGs GE Marquette SL12 algorithm (version 23 (v243)) and a cardiologist with training in the interpretation of athlete's ECG.

The waveform files are stored in .dat -files with a corresponding .hea file containing all the metadata. This file formats are compatible with the Python WaveForm DataBase (WFDB) package and this makes it easy to import the data.

All ECG waveforms are sampled and stored with a sampling frequency of 500Hz and a length of 5000 samples (10 seconds). The header file contains information about the total amount of leads, samples per lead and additional information about each lead. The last two lines in the header file include the diagnosis given by the Marquette SL12 (SL12) algorithm and the cardiologist (C).

Context

This is a multivariate type of dataset which means providing or involving a variety of separate mathematical or statistical variables, multivariate numerical data analysis. It is composed of 14 attributes which are age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, old peak — ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels and Thalassemia. This database includes 76 attributes, but all published studies relate to the use of a subset of 14 of them. The Cleveland database is the only one used by ML researchers to date. One of the major tasks on this dataset is to predict based on the given attributes of a patient whether that particular person has heart disease or not and the other is the experimental task to diagnose and find out various insights from this dataset which could help in understanding the problem more.

Content

1. age (Age of the patient in years)
2. sex (Male/Female)
3. cp chest pain type ([typical angina, atypical angina, non-anginal, asymptomatic])
4. trestbps resting blood pressure (resting blood pressure (in mm Hg on admission to the hospital))
5. chol (serum cholesterol in mg/dl)
6. fbs (if fasting blood sugar > 120 mg/dl)
7. restecg resting electrocardiographic results ([normal, stt abnormality, lv hypertrophy])
8. thalach maximum heart rate achieved
9. exang exercise-induced angina (True/ False)
10. oldpeak ST depression induced by exercise relative to rest
11. slope the slope of the peak exercise ST segment
12. ca number of major vessels (0-3) colored by fluoroscopy
13. thal [normal; fixed defect; reversible defect]
14. target the predicted attribute ## Creators
15. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
16. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
17. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
18. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

ECG images dataset of Cardiac and COVID-19 Patients created under the auspices of Ch. Pervaiz Elahi Institute of Cardiology Multan, Pakistan that aims to help the scientific community for conducting the research for COVID-19 and Cardiovascular diseases.

The peer-reviewed paper associated with this dataset has now been published in Scientific Data, and can be accessed here: https://www.nature.com/articles/sdata201820. Please cite this when using the dataset.

Open clinical trial data provide a valuable opportunity for researchers worldwide to assess new hypotheses, validate published results, and collaborate for scientific advances in medical research. Here, we present a health dataset for the non-invasive detection of cardiovascular disease (CVD), containing 657 data records from 219 subjects. The dataset covers an age range of 20–89 years and records of diseases including hypertension and diabetes. Data acquisition was carried out under the control of standard experimental conditions and specifications. This dataset can be used to carry out the study of photoplethysmograph (PPG) signal quality evaluation and to explore the intrinsic relationship between the PPG waveform and cardiovascular disease to discover and evaluate latent characteristic information contained in PPG signals. These data can also be used to study early and noninvasive screening of common CVD such as hypertension and other related CVD diseases such as diabetes.

The effect of ginkgolide C on cardiac function in rats with ischemia-reperfusion injury