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.

Analysis of ‘Heart Disease UCI’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ronitf/heart-disease-uci on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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.

Content

Attribute Information:

1. age
   
2. sex
   
3. chest pain type (4 values)
   
4. resting blood pressure
   
5. serum cholestoral in mg/dl
   
6. fasting blood sugar > 120 mg/dl
   
7. resting electrocardiographic results (values 0,1,2)
   
8. maximum heart rate achieved
   
9. exercise induced angina
   
10. oldpeak = ST depression induced by exercise relative to rest
    
11. the slope of the peak exercise ST segment
    
12. number of major vessels (0-3) colored by flourosopy
    
13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
    

The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. One file has been "processed", that one containing the Cleveland database. All four unprocessed files also exist in this directory.

To see Test Costs (donated by Peter Turney), please see the folder "Costs"

Acknowledgements

Creators:
1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

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.

--- Original source retains full ownership of the source dataset ---

This Dataset is Copied From the Orignal Dataset. This Dataset is Preprocess with Advance Method. This Dataset is Cleaned From Missing Values.

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, oldpeak — 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 that whether that particular person has heart disease or not and other is the experimental task to diagnose and find out various insights from this dataset which could help in understanding the problem more.

Content

Column Descriptions: - id (Unique id for each patient) - age (Age of the patient in years) - origin (place of study) - sex (Male/Female) - cp chest pain type ([typical angina, atypical angina, non-anginal, asymptomatic]) - trestbps resting blood pressure (resting blood pressure (in mm Hg on admission to the hospital)) - chol (serum cholesterol in mg/dl) - fbs (if fasting blood sugar > 120 mg/dl) - restecg (resting electrocardiographic results) -- Values: [normal, stt abnormality, lv hypertrophy] - thalach: maximum heart rate achieved - exang: exercise-induced angina (True/ False) - oldpeak: ST depression induced by exercise relative to rest - lope: the slope of the peak exercise ST segment - ca: number of major vessels (0-3) colored by fluoroscopy - thal: [normal; fixed defect; reversible defect] - num: the predicted attribute

Acknowledgements

Creators:

1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Relevant Papers:

• Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304--310. Web Link
• David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease presence with the Cleveland database." Web Link
• Gennari, J.H., Langley, P, & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11-61. Web Link

Citation Request:

The authors of the databases have requested that any publications resulting from the use of the data include the names of the principal investigator responsible for the data collection at each institution. They would be:

• 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., Ph.D.

Model 1 was a single-factor analysis model; model 2 was adjusted for age, gender, alcohol consumption, income, education and history of cardiovascular disease on the basis of model 1; model 3 was further adjusted for heart rate, uric acid, and high-sensitivity CRP on the basis of model 2.CVH score, Cardiovascular Health Score; HR, hazard ratio; CI, confidence interval.Hazard Ratios (95% CI) of Incidence of Total CVD Events, Myocardial Infarction, and Stroke among Different Groups According to the CVH Score at Baseline.

Context

The dataset is the Cleveland Heart Disease dataset taken from the UCI repository. The dataset consists of 303 individuals’ data. There are 14 columns in the dataset(which have been extracted from a larger set of 75). No missing values. The classification task is to predict whether an individual is suffering from heart disease or not. (0: absence, 1: presence)

original data: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Content

This database contains 13 attributes and a target variable. It has 8 nominal values and 5 numeric values. The detailed description of all these features are as follows:

1. Age: Patients Age in years (Numeric)
2. Sex: Gender (Male : 1; Female : 0) (Nominal)
3. cp: Type of chest pain experienced by patient. This term categorized into 4 category. 0 typical angina, 1 atypical angina, 2 non- anginal pain, 3 asymptomatic (Nominal)
4. trestbps: patient's level of blood pressure at resting mode in mm/HG (Numerical)
5. chol: Serum cholesterol in mg/dl (Numeric)
6. fbs: Blood sugar levels on fasting > 120 mg/dl represents as 1 in case of true and 0 as false (Nominal)
7. restecg: Result of electrocardiogram while at rest are represented in 3 distinct values 0 : Normal 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) 2: showing probable or definite left ventricular hypertrophyby Estes' criteria (Nominal)
8. thalach: Maximum heart rate achieved (Numeric)
9. exang: Angina induced by exercise 0 depicting NO 1 depicting Yes (Nominal)
10. oldpeak: Exercise induced ST-depression in relative with the state of rest (Numeric)
11. slope: ST segment measured in terms of slope during peak exercise
    0: up sloping; 1: flat; 2: down sloping(Nominal)
12. ca: The number of major vessels (0–3)(nominal)
13. thal: A blood disorder called thalassemia 0: NULL 1: normal blood flow 2: fixed defect (no blood flow in some part of the heart) 3: reversible defect (a blood flow is observed but it is not normal(nominal)
14. target: It is the target variable which we have to predict 1 means patient is suffering from heart disease and 0 means patient is normal.

Variable to be predicted

Absence (1) or presence (2) of heart disease

Cost Matrix

 abse pres

absence 0 1 presence 50

where the rows represent the true values and the columns the predicted.

No missing values.

303 observations

Acknowledgements

Creators: 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

similar dataset : https://www.kaggle.com/datasets/ritwikb3/heart-disease-statlog

Analysis of ‘Heart Disease Cleveland UCI’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/cherngs/heart-disease-cleveland-uci on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The data is already presented in https://www.kaggle.com/ronitf/heart-disease-uci but there are some descriptions and values that are wrong as discussed in https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877. So, here is re-processed dataset that was cross-checked with the original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

Content

There are 13 attributes 1. age: age in years 2. sex: sex (1 = male; 0 = female) 3. cp: chest pain type -- Value 0: typical angina -- Value 1: atypical angina -- Value 2: non-anginal pain -- Value 3: asymptomatic 4. trestbps: resting blood pressure (in mm Hg on admission to the hospital) 5. chol: serum cholestoral in mg/dl 6. fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) 7. restecg: resting electrocardiographic results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria 8. thalach: maximum heart rate achieved 9. exang: exercise induced angina (1 = yes; 0 = no) 10. oldpeak = ST depression induced by exercise relative to rest 11. slope: the slope of the peak exercise ST segment -- Value 0: upsloping -- Value 1: flat -- Value 2: downsloping 12. ca: number of major vessels (0-3) colored by flourosopy 13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect and the label 14. condition: 0 = no disease, 1 = disease

Acknowledgements

Data posted on Kaggle: https://www.kaggle.com/ronitf/heart-disease-uci Description of the data above: https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877 Original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbr 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., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Inspiration

With the attributes described above, can you predict if a patient has heart disease?

--- Original source retains full ownership of the source dataset ---

Cardiovascular Disease Deaths, Small Areas by Sex, 1999 to 2011 - CVDSASEX

Summary: Number of deaths and rates of deaths per 100,000 (age adjusted) due to Cardiovascular disease over the 13 year period from 1999 to 2011; with person year and mean annual populations, for each sex and the total population, for all 109 NM Small Area geographies.

Prepared by: Bryan Patterson, bryan.patterson@state.nm.us, 505-476-9228

INCLUDES: Cardiovascular Disease ICD-10 used by American Heart Association (I00-I99, Q20-Q29)

Data Sources: New Mexico Death Certificate Database, Office of Vital Records and Statistics, New Mexico Department of Health; Population Estimates: University of New Mexico, Geospatial and Population Studies (GPS) Program, http://bber.unm.edu/bber_research_demPop.html. Retrieved Thurs, 26 June 2014 from New Mexico Department of Health, Indicator-Based Information System for Public Health Web site: http://ibis.health.state.nm.us

Shapefile:

Feature:

Master File:

NM Data Variable Definition

999 SANo NM Small Area Number

NEW MEXICO SAName NM Small Area Name

56922 DB Number of Cardiovascular Disease Deaths, Both Sexes, 1999-2011

28502 DF Number of Cardiovascular Disease Deaths, Females, 1999-2011

28420 DM Number of Cardiovascular Disease Deaths, Males, 1999-2011

25270112 PB Population, Person-Years, Both Sexes, 1999-2011

12789365 PF Population, Person-Years, Females, 1999-2011

12480747 PM Population, Person-Years, Males, 1999-2011

1943855 MAPB Mean Annual Population, Person-Years, Both Sexes, 1999-2011

983797 MAPF Mean Annual Population, Person-Years, Females, 1999-2011

960057 MAPM Mean Annual Population, Person-Years, Males, 1999-2011

231 RB Rate per 100,000 of Cardiovascular Disease Deaths, Both Sexes, 1999-2011

198.8 RF Rate per 100,000 of Cardiovascular Disease Deaths, Females, 1999-2011

267.6 RM Rate per 100,000 of Cardiovascular Disease Deaths, Males, 1999-2011

ObjectivesTo examine national trends in unhealthy lifestyle factors among adults with cardiovascular disease (CVD) in the United States (US) between 1999 and 2018.MethodsWe analyzed data from National Health and Nutrition Examination Survey (NHANES), a nationally representative survey of participants with CVD who were aged ≥20 years, which was conducted between 1999 and 2000 and 2017–2018. CVD was defined as a self-report of congestive heart failure, coronary heart disease, angina, heart attack, or stroke. The prevalence rate of each unhealthy lifestyle factor was calculated among adults with CVD for each of the 2-year cycle surveys. Regression analyses were used to assess the impact of sociodemographic characteristics (age, sex, race/ethnicity, family income, education level, marital status, and employment status).ResultsThe final sample included 5610 NHANES respondents with CVD. The prevalence rate of their current smoking status remained stable among respondents with CVD between 1999 and 2000 and 2017–2018. During the same period, there was a decreasing trend in the age-adjusted prevalence rate of poor diet [primary American Heart Association (AHA) score

The data in this Zenodo entry corresponds to the data used to produce the results in https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169. The zipped folder contains three files

• Metabolite Data.csv - The meatobilte measurements for all the samples
• Clinical Data.csv - Values for the clinical variables
• Clinical Data Descriptions.csv - More in depth explanation of clinical variables as well as possible values of the variables

This study was supported by the American Heart Association (AHA20CDA35310498 and AHA18IPA34170070) and the National Institutes of Health (NIH/NCATS Colorado CTSA, No. UL1 TR001082 and NIH/NHLBI K23HL12363

According to Cognitive Market Research, the global Cardiovascular Ultrasound System market size is USD 1.42 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 5.45% from 2024 to 2031. Market Dynamics of Cardiovascular Ultrasound System Market

Key Drivers for Cardiovascular Ultrasound System Market

Rising Incidence of Cardiovascular Illnesses- One of the main reasons the Cardiovascular Ultrasound System market is growing is the rising incidence of cardiovascular illnesses. According to the World Health Organization's (WHO) June 2021 update, cardiovascular diseases are the main cause of death in low- and middle-income nations, accounting for roughly three-quarters of all CVD deaths. As a result of the high mortality rate, the demand for illness diagnoses and monitoring is increasing, which will drive expansion in the cardiovascular ultrasound system market. Furthermore, according to the 2022 American Heart Association (AHA) report, approximately 244.1 million people worldwide were living with ischemic heart disease (IHD), with North Africa and the Middle East, Central and South Asia, and Eastern Europe having the highest prevalence rates of IHD in 2020. This burden of cardiovascular diseases is expected to rise further due to the high incidence of associated risk factors such as obesity, hypertension, diabetes, smoking, etc, as well as sedentary lifestyles. Thus, the application of ultrasonography in illness diagnoses, monitoring, and therapy is anticipated to rise, driving growth in the cardiovascular ultrasound system market during the forecast period.
Rising preference for non-invasive diagnostic procedures is anticipated to drive the Cardiovascular Ultrasound System market's expansion in the years ahead.

Key Restraints for Cardiovascular Ultrasound System Market

High cost of cardiovascular ultrasound system devices
Dearth of skilled and experienced sonographers

Introduction of the Cardiovascular Ultrasound System Market

The cardiovascular ultrasound system market is expanding Quickly due to rising rates of heart disease, advances in ultrasound technology, and increased demand for non-invasive diagnostic methods. Cardiovascular ultrasound, also known as echocardiography, is a vital diagnostic tool that uses sound waves to create images of the coronary arteries and blood vessels. These technologies are vital in modern cardiology because they help diagnose and manage a variety of heart diseases. Additionally, continuous advances in ultrasound technology, including 3D and 4D imaging, Doppler ultrasound, and contrast-enhanced ultrasound, have considerably improved the capabilities and accuracy of cardiovascular ultrasound systems. These developments improve diagnostic precision, allowing for greater visualization of cardiac structures and blood flow, resulting in increased market growth. The global aging population greatly increases the demand for cardiovascular diagnostic technologies. Older persons are more prone to cardiovascular problems, necessitating regular monitoring and diagnosis. As the older population develops, so will the demand for cardiovascular ultrasound equipment.

CRDC = Chart Review, Diagnostic Criteria – the charts of potential cases were reviewed, and a formal set of diagnostic criteria were applied when evaluated cases.CRMD = Chart Review, Medical Doctor – the charts of potential cases were reviewed by a physician, who evaluated cases using their clinical judgement or an otherwise unspecified set of criteria.AHA = American Heart Association; MONICA =  MONItoring Trends and Determinants in CArdiovascular Disease; WHO = World Health Organization.

Snapshot img

Prosthetic Heart Valves Market Size 2024-2028

The prosthetic heart valves market size is forecast to increase by USD 4.83 billion at a CAGR of 10.03% between 2023 and 2028.

The market is experiencing significant growth due to the rising prevalence of heart disorders, particularly among the geriatric population. This trend is driven by the increasing incidence of cardiovascular diseases and the subsequent need for heart valve replacement. Another trend influencing the market is the development of MRI-conditional heart valves, which enable patients to undergo magnetic resonance imaging procedures without the risk of valve damage. However, complications associated with prosthetic heart valve replacement, such as blood clots and the need for long-term anticoagulation therapy, pose challenges to market growth. Additionally, factors like medical tourism, the adoption of minimally invasive procedures, and the increasing availability of skilled healthcare professionals are shaping the market dynamics. Furthermore, advancements in wearable devices and telemedicine are enabling remote monitoring and early detection of valve-related issues, improving patient outcomes and reducing healthcare costs.

What will be the Size of the Prosthetic Heart Valves Market During the Forecast Period?

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The market represents a significant segment in the medical technology industry, catering to the demand for solutions addressing various cardiovascular diseases, particularly valvular heart diseases. This market is driven by several factors, including an aging population, increasing prevalence of cardiovascular diseases, and advancements in medical technology. Cardiovascular diseases, including heart valve diseases such as mitral valve insufficiency and mitral regurgitation, pose a substantial health risk to the population. According to the American Heart Association, heart disease is the leading cause of death In the US. They are essential medical devices used to replace damaged or diseased heart valves, ensuring proper blood flow within the heart.
The market encompasses various types, including mechanical heart valves and biological valves. Mechanical heart valves are typically made of materials like titanium and are known for their durability, while biological valves are derived from animal tissue or human donor tissue. Both types cater to specific patient needs, with mechanical valves offering longer lifespans but requiring regular anticoagulation and biological valves more closely mimicking the natural heart valve. Advancements in medical technology have led to the emergence of minimally invasive surgical interventions and transcatheter heart valve replacement procedures. These innovations have made heart valve replacement surgeries less invasive and more accessible to a broader patient population.

How is this Prosthetic Heart Valves Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Transcatheter heart valves
  Tissue heart valves
  Mechanical heart valves


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

By Product Insights

The transcatheter heart valves segment is estimated to witness significant growth during the forecast period.

The global market is experiencing significant growth due to the increasing prevalence of heart valve diseases, particularly among the geriatric population. Approximately 5% of the world's population aged 65 and above are at risk of developing valvular heart diseases, such as mitral regurgitation and aortic stenosis. As people age, heart muscles lose elasticity, making it more challenging for them to manage different blood pressure rates. This results In the need for replacement or repair procedures. Moreover, advancements in medical technology have led to minimally invasive procedures, wearable devices, and telemedicine, which have increased accessibility to healthcare services.

These innovations have contributed to the growth of the market, making procedures more convenient and less invasive for patients. Skilled healthcare professionals are also increasingly using titanium and carbon prosthetic heart valves due to their durability and effectiveness in preventing blood clots. Medical tourism is another factor driving the growth of the market, as patients travel to countries with advanced healthcare facilities and lower costs for treatment. The market is expected to continue growing as more people require heart valve procedures due to aging populations and the rising prevalence of heart valve diseases.

Get a glance at the market report of share of various segments R

Electrocardiogram (ECG) Device Market Outlook

The global Electrocardiogram (ECG) device market size was valued at approximately $8.5 billion in 2023 and is projected to grow to $14.9 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 6.3% during the forecast period. The major growth factor driving this market is the increasing prevalence of cardiovascular diseases worldwide, coupled with technological advancements in ECG devices that enhance diagnostic accuracy and patient monitoring capabilities.

The rising incidence of cardiovascular diseases (CVDs) is a primary growth factor for the ECG device market. According to the World Health Organization, CVDs are the leading cause of death globally, taking an estimated 17.9 million lives each year. This alarming statistic underscores the need for effective diagnostic tools like ECG devices, which are crucial for early detection and management of heart conditions. Furthermore, an aging global population, which is more susceptible to heart diseases, is expected to contribute significantly to the market growth. As healthcare systems worldwide strive to improve outcomes for patients with chronic health conditions, the demand for advanced diagnostic tools like ECG devices is set to increase.

Technological advancements are another significant driver of the ECG device market. Innovations such as wireless and portable ECG devices have revolutionized patient monitoring by making it possible to conduct ECG tests outside of traditional healthcare settings. These advancements not only enhance patient convenience but also enable continuous monitoring, which is crucial for patients with intermittent symptoms. Additionally, the integration of artificial intelligence (AI) and machine learning algorithms in ECG devices is enhancing diagnostic accuracy and predictive analytics, further propelling market growth. The ability to provide real-time data and early warning signals is making ECG devices indispensable in modern healthcare.

Government initiatives aimed at reducing the burden of cardiovascular diseases are also fueling the growth of the ECG device market. Many countries are implementing screening programs and investing in healthcare infrastructure to improve the diagnosis and treatment of heart conditions. For instance, the American Heart Association (AHA) and similar organizations in other countries are actively promoting the use of ECG devices as part of routine health check-ups. Additionally, favorable reimbursement policies in developed countries are encouraging the adoption of advanced ECG devices, thereby driving market growth.

Regional outlook for the ECG device market reveals significant opportunities across various geographies. North America currently holds the largest market share, driven by high healthcare expenditure, advanced healthcare infrastructure, and a high prevalence of cardiovascular diseases. Europe follows closely, benefiting from a strong focus on healthcare quality and patient safety. The Asia-Pacific region is expected to witness the highest growth rate, driven by improving healthcare infrastructure, increasing healthcare expenditure, and rising awareness about heart health. Latin America and the Middle East & Africa, although smaller in market size, are also showing promising growth due to increasing investments in healthcare and rising prevalence of lifestyle-related heart conditions.

Product Type Analysis

The ECG device market is segmented into various product types, each catering to specific diagnostic needs and patient scenarios. Resting ECG devices are among the most commonly used types, providing critical insights into the heart's electrical activity while the patient is at rest. These devices are essential for routine check-ups and initial diagnosis of heart conditions. Resting ECG devices are widely used in hospitals, clinics, and other healthcare settings, making them a staple in cardiac diagnostics. Their reliability and ease of use make them indispensable tools for healthcare professionals.

Stress ECG devices are another important segment, primarily used to monitor the heart's activity under physical stress. These devices are crucial for diagnosing conditions that may not be apparent during resting conditions, such as exercise-induced arrhythmias and ischemia. Stress ECG devices are commonly used in cardiac rehabilitation centers and sports medicine facilities. They provide valuable information that aids in the development of treatment plans and fitness regimes for patients and athletes alike. The increasing focus on preventive healthcare and fitness is expected to

Introduction Abnormality of cardiac conduction system can induce arrhythmia. Abnormal heart rhythm can lead to other cardiac diseases and complications, and can be life-threatening [1]. There are various types of arrhythmias and each type is associated with a pattern, and as such, it is possible to be identified. Arrhythmias can be classified into two major categories. The first category consists of arrhythmias formed by a single irregular heartbeat in electrocardiogram (ECG), herein called morphological arrhythmia, while another category consists of arrhythmias formed by a set of irregular heartbeats in ECG, herein called rhythmic arrhythmias [2]. Dynamic electrocardiogram (DCG), like ECG Holter, provides an important way to monitor the incidences of arrhythmias in daily life, facilitating the doctors to check a total number and distribution of arrhythmias in a long time and thus to provide the required therapy to prevent further problems. The 3rd China Physiological Signal Challenge 2020 (CPSC 2020) aims to encourage the development of algorithms for searching for premature ventricular contraction (PVC) and supraventricular premature beat (SPB) from 24-hour dynamic single-lead ECG recordings usually with low signal quality and/or abnormal rhythm waveforms. Similar the previous works and efforts of the CPSC 2018 [3] and CPSC 2019 [4], accurate locating of abnormal heartbeats is another critical issue put forward here for further discussion. ECG signal provides an important role in non-invasively monitoring and clinical diagnosis for cardiovascular disease (CVD). Arrhythmia detection is one of the ultimate goals of routine ECG monitoring, and PVC and SPB are the two most common arrhythmias. Increase in these beats may be a precursor to stroke or sudden cardiac death [5]. Although their detection methods have been severely tracked throughout the last several decades, accurate and robust detections are still challenging in noisy or low-signal quality environment, especially for daily monitored ECG waveforms. It is true that many of the developed PVC and SPB detection algorithms can achieve high accuracy (over 96% in sensitivity and positive predictivity) when tested over the standard ECG databases such as the MIT-BIH Arrhythmia Database or AHA Database [6]. However, these algorithms may fail when used in the noisy environment. Especially, even the basic QRS detection can be invalid in the low signal quality ECG analysis [7]. A recent study confirmed that none of the common QRS detection algorithms can obtain 80% detection accuracy when tested in a dynamic noisy ECG database. In this year’s challenge, we provide a new ECG database containing long-term noisy ECG recordings from clinical arrhythmia patients, to encourage the participants to develop more efficient and robust algorithms for PVC and SPB detection.

Challenge Data Training data consists of 10 single-lead ECG recordings collected from arrhythmia patients, each of the recording last for about 24 hours (shown in Table 1). Table 1 also indicates the patient if he/she is an atrial fibrillation (AF) patient. Test set contains similar ECG recordings, which is unavailable to public and will remain private for the purpose of scoring for the duration of Challenge and for some period afterwards. All data were collected by a unified wearable ECG device with a sampling frequency of 400 Hz, and provided in MATLAB format (each including three *.mat file: one is ECG data and another two are the corresponding PVC and SPB annotation files, respectively).

Detailed information of training data.

Reference [1] S. L. Oh, E. Y. Ng, R. San Tan, and U. R. Acharya, "Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats," Computers in biology and medicine, vol. 102, pp. 278-287, 2018. [2] E. J. D. S. Luz, W. R. Schwartz, G. Cámara-Chávez, and D. Menotti, "ECG-based heartbeat classification for arrhythmia detection: A survey," Computer methods and programs in biomedicine, vol. 127, pp. 144-164, 2016. [3] F. Liu, C. Liu, L. Zhao, X. Zhang, X. Wu, X. Xu, Y. Liu, C. Ma, S. Wei, Z. He, J. Li, and E. Y. K. Ng, "An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection," Journal of Medical Imaging and Health Informatics, vol. 8, pp. 1368-1373, 2018. [4] H. Gao, C. Liu, X. Wang, L. Zhao, Q. Shen, E. Y. K. Ng, and J. Li, "An Open-Access ECG Database for Algorithm Evaluation of QRS Detection and Heart Rate Estimation," Journal of Medical Imaging and Health Informatics, vol. 9, pp. 1853-1858, 2019. [5] J. Oster, J. Behar, O. Sayadi, S. Nemati, A. E. Johnson, and G. D. Clifford, "Semisupervised ECG ventricular beat classification with novelty detection based on switching Kalman filters," IEEE Transactions on Biomedical Engineering, vol. 62, pp. 2125-2134, 2015. [6] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. Peng, and H. E. Stanley, "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," Circulation, vol. 101, pp. e215-e220, 2000. [7] F. Liu, C. Liu, X. Jiang, Z. Zhang, Y. Zhang, J. Li, and S. Wei, "Performance analysis of ten common QRS detectors on different ECG application cases," Journal of Healthcare Engineering, vol. 2018, pp. 9050812(1)-9050812(8), 2018. [8] ANSI/AAMI EC57, "1998 / (R) 2008-Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms", Arlington, VA, USA, 2008.

Analysis of Coronary Artery Disease Risk Factors

Impact of Sex, Age, and Other Contributing Factors

By IBM Watson AI XPRIZE - Health [source]

About this dataset

Welcome to the UCI Machine Learning Repository, where you can find hundreds of datasets curated by experienced researchers for use in predictive and inferential analyses. Today, we will explore a dataset covering coronary artery disease risk factors from the Swiss Medical Center and V.A. Medical Center in Long Beach and Cleveland Clinic Foundation. This repository, collected by David W. Aha of the University of California at Irvine's School of Information and Computer Science, contains information about patients with coronary artery disease including age, sex, blood pressure, cholesterol levels, and other key variables that might be pertinent to risk assessment or prediction of outcomes for different interventions or therapies—all important factors when it comes to cardiovascular health!

Included in this dataset are attributes such as age (in years), sex (1 = male; 0 = female), chest pain type (1 = typical angina; 2 = atypical angina; 3 = non-anginal pain); 4 = asymptomatic), exercise induced angina (1 = yes; 0 = no), number of major vessels (0-3) colored by flourosopy , fasting blood sugar > 120 mg/dl (1= true; 0= false) ST depression induced by exercise relative to rest maximum heart rate achieved ,and target (0= no disease; 1-4 increasing severity). The names and social security numbers were removed from the original database but dummy values have replaced them for identification purposes. This dataset offers an opportunity for clinical professionals as well as machine learning experts alike to perform predictive analytics on large populations quickly helping medical providers make data driven decisions in order to improve patient care across diagnoses through analyzing patient characteristics - care that can mean life or death for someone suffering with cardiovascular diseases like heart failure due to clogged arteries! Are you up for the task?

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How to use the dataset

This dataset contains information on patients at risk for coronary artery disease (CAD). The data includes medical records of the patient's age, sex, blood pressure, cholesterol levels,smoking history and other risk factors. It is suitable for researchers who are interested in exploring potential risk factors that are linked with CAD.

The analysis of this dataset requires a few simple steps: - Read through the dataset to familiarize yourself with the available data points. Understanding the relevant information will help you to gain insights from your analysis. - Clean and prepare your data so it is in a usable format by removing any unnecessary columns or attributes that are not relevant to your research question(s). This step will help simplify your dataset so you can focus on specific points during your analysis. - Choose a statistical technique such as linear regression or logistic regression to analyze relationships between different attributes (e.g., age, gender and other risk factors) and determine which ones contribute most significantly to CAD development or advancement. Visualization tools such as scatter plots can also be used if desired for further exploration into relationships between variables/attributes
4 Identify patterns or correlations found in the relationships between variables/attributesin order that these can be used as independent predictors of CAD within an overall risk assessment frameworkfor example using decision trees or neural networks. You may needto use additional techniques like machine learning algorithms to support modeling predictions if required; howeverthis depends onthe research objectives beinginvestigated.]

Research Ideas

• Using this dataset, researchers could analyze the correlation between age, chest pain type, exercise induced angina and maximum heart rate achieved to identify patients at risk of developing coronary artery disease.
• The dataset could be used to explore the impact of family history of coronary artery disease on a person’s risk for developing this condition.
• The data can be used to develop and test machine learning algorithms that can accurately predict heart attack or stroke risks in individuals with certain characteristics or thresholds in various attributes such as resting blood pressure levels, cholesterol levels, etc.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

Li...

Background: The recent American College of Cardiology/American Heart Association (ACC/AHA) guidelines redefined blood pressure levels 130-139/80-89 mmHg as stage 1 hypertension. However, the association of stage 1 hypertension with cardiovascular disease (CVD) and its age-specific differences among the rural women in Liaoning province remains unclear. It needs to be quantified in considering guideline adoption in China.Methods: In total, 19,374 women aged ≥35 years with complete data and no cardiovascular disease at baseline were followed in a rural community-based prospective cohort study of Liaoning province, China. Follow-up for the new cases of CVD was conducted from the end of the baseline survey to the end of the third follow-up survey (January 1, 2008–December 31, 2017). Adjusted Cox proportional hazards models were applied to estimate the Hazard Ratios (HR) and 95% Confidence Intervals (CI) with the normal blood pressure as a reference.Results: During the median follow-up period of 12.5 years, 1,419 subjects suffered all-cause death, 748 developed CVD, 1,224 participants suffered stroke and 241 had Myocardial Infarction (MI). Compared with normal BP, Stage 1 hypertension had a HR (95% CI) of 1.694 (1.202–2.387) in CVD mortality, 1.575 (1.244–1.994) in the incidence of stroke. The results obtained that the risk of CVD mortality and incidence of stroke was significantly associated with stage 1 hypertension in rural women aged ≥45 years after adjusting for other potential factors. However, in participants aged 35–44 years, stage 1 hypertension was not associated with an increased risk of cardiovascular disease.Conclusions: The newly defined stage 1 hypertension is associated with an increased risk of CVD mortality and also incidence of stroke in the rural women aged ≥45 years population of Liaoning province. This study can be a good reference for health policy makers and clinicians workers to make evidence-based decisions toward lowering burden of cardiovascular disease more efficient, timely measures on prevention and control of stage 1 hypertension in China.

Protein-Protein, Genetic, and Chemical Interactions for FitzGerald GA (2007):COX-2 in play at the AHA and the FDA. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The inhibition of cyclooxygenase (COX)-2 confers a small but absolute risk of cardiovascular events on patients taking nonsteroidal anti-inflammatory drugs (NSAIDs). This risk has been established by placebo-controlled trials of NSAIDs selective for COX-2; traditional NSAIDs seem to be heterogeneous with respect to cardiovascular risk. The American Heart Association (AHA) has proposed a 'stepped-care' approach to the use of NSAIDs in patients with cardiovascular disease, whereas the Food and Drug Administration (FDA) has failed to approve the COX-2-selective NSAID etoricoxib. In this article, these actions of the AHA and the FDA are interpreted in the light of current knowledge, prompting the formulation of scientific questions that might be addressed.

Introduction

Google Fit Statistics: Google Fit, since its launch in 2014, formed the major platform of fitness and health for Google, enabling users to track several health metrics and pool data from several fitness apps and devices. In its continued evolution were added unique features like Heart Points, developed under the auspices of WHO and AHA, aimed at inducing physical activity.

Changes of much significance are due in 2024, marking a change in Google's very own approach to health data-keeping. In this article, we will enclose the Google Fit statistics.

Introduction The China Physiological Signal Challenge 2019 (CPSC 2019) aims to encourage the development of algorithms for challenging QRS detection and heart rate (HR) estimation from short-term single-lead ECG recordings usually with low signal quality and/or abnormal rhythm waveforms.

ECG signal provides an important role in non-invasively monitoring and clinical diagnosis for cardiovascular disease (CVD). Detection of QRS complex is an essential step for ECG signal processing, and can benefit the following HR calculation and abnormal situation analysis. Although detection methods of QRS complex have been severely tracked throughout the last several decades, accurate QRS location and HR estimation are still challenging in noisy signal episode or abnormal rhythm waveforms, especially when the ECG recordings are from the wearable dynamic ECG acquisition. It is true that many of the developed QRS detection algorithms can achieve high accuracy (over 99% in sensitivity and positive predictivity) when tested over the standard ECG databases such as MIT-BIH Arrhythmia Database or AHA Database [1]. However, these algorithms may not be able to perform well when used in the daily life environment that will cause severe noises and significantly reduce the signal quality. A recent study confirmed that none of the common QRS algorithms can obtain 80% detection accuracy when tested in a common dynamic noisy ECG database [2]. Thus, in this challenge, we provide a new ECG database containing noisy ECG episodes and/or signals with different arrhythmia patterns, encouraging participants to develop more efficient and robust algorithms QRS detection and HR estimation. In addition, it is worth to note that, although HR can be calculated from the detection results of QRS complexes, HR can be still estimated without QRS detection step [3,4].

Challenge Data Training data consists of 2,000 single-lead ECG recordings collected from patients with cardiovascular disease (CVD), each of the recording last for 10 s. Test set contains similar ECG recordings of same lengths, which is unavailable to public and will remain private for the purpose of scoring for the duration of the Challenge and for some period afterwards. ECG recordings were obtained from multiple sources using a variety of instrumentation, although in all cases they are presented as 500 Hz sample rate here. All recordings were provided in MATLAB format (each including two .mat file: one is ECG data and another one is the corresponding QRS annotation file). Pan &Tompkins (P&T) algorithm [5,6] is also provided as benchmark or comparable algorithm.

Although QRS detection and HR estimation are widely studied by lots of researchers for many years, accurate detection is still really challenging in this Challenge due to the QRS amplitude variation, QRS morphological variation, and occurrence of intense variability in the intervals between beats, different arrhythmias, as well as noises.

Reference

G.B. Moody, R.G. Mark, The impact of the MIT-BIH arrhythmia database, IEEE Engineering in Medicine & Biology Magazine the Quarterly Magazine of the Engineering in Medicine & Biology Society, 20 (2001) 45-50. Liu, F.F.; Wei, S.S.; Li, Y.B.; Jiang, X.E.; Zhang, Z.M.; Liu, C.Y., Performance analysis of ten common qrs detectors on different ecg application cases. Journal of Healthcare Engineering 2018, 2018, ID 9050812. J.J. Gieraltowski, K. Ciuchcinski, I. Grzegorczyk, K. Kosna, Heart rate variability discovery: Algorithm for detection of heart rate from noisy, multimodal recordings, Computing in Cardiology, 2014, pp. 253-256. J. Gieraltowski, K. Ciuchcinski, I. Grzegorczyk, K. Kosna, M. Solinski, P.Podziemski, RS slope detection algorithm for extraction of heart rate from noisy, multimodal recordings, Physiological Measurement, 36 (2015) 1743-1761. P.S. Hamilton, W.J. Tompkins, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database, Biomedical Engineering, IEEE Transactions on, (1986) 1157-1165. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm, Biomedical Engineering, IEEE Transactions on, (1985) 230-236. ANSI-AAMI (1998). Testing and reporting performance results of cardiac rhythm and st segment measurement algorithms, ANSI-AAMI:EC57.

According to Cognitive Market Research, the global laser atherectomy devices market size will be USD 201.5 million in 2025. It will expand at a compound annual growth rate (CAGR) of 8.00% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 80.60 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 60.45 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 46.35 million in 2025 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2025 to 2033.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 10.08 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2025 to 2033.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 4.03 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2025 to 2033.
The peripheral vascular category led the laser atherectomy devices market.

Market Dynamics of Laser Atherectomy Devices Market

Key Drivers for Laser Atherectomy Devices Market

Rising Prevalence of Peripheral and Coronary Artery Diseases to Boost Market Growth

The increasing incidence of peripheral and coronary artery diseases (PAD and CAD) is a major driver for the laser atherectomy devices market. Factors such as ageing populations, sedentary lifestyles, unhealthy diets, smoking, and the global rise in diabetes and obesity contribute significantly to the growing patient pool. PAD and CAD often result in arterial blockages that, if untreated, can lead to severe complications like limb ischemia, myocardial infarction, or stroke. Laser atherectomy devices provide a minimally invasive treatment option to remove plaque and restore blood flow effectively. Their precision and reduced risk of vascular injury make them a preferred choice for treating complex lesions. As awareness of vascular diseases increases and early diagnosis becomes more common, the demand for advanced laser-based treatments is expected to grow, further driving the adoption of these devices in both developed and emerging healthcare markets. For instance, According to extensive research that led to a new AHA Presidential Advisory, the prevalence of cardiovascular disease and many of its risk factors is expected to continue to climb over the next 30 years. By 2050, cardiovascular disease is expected to increase from 11.3% to 15% of the population, affecting up to 45 million U.S. adults. Stroke prevalence is expected to double, from 10 million to almost 20 million adults. Obesity – a major risk factor for cardiovascular disease – is predicted to climb from 43% of the population to more than 60%. These findings serve as a call to arms and a warning that we must better control risk factors to avoid the oncoming tsunami of cardiometabolic disease.

(https://www.heart.org/en/around-the-aha/aha-names-biggest-advances-in-cardiovascular-research-for-2024)

Technological Advancements in Laser Atherectomy Devices to Drive Market Growth

Ongoing technological advancements in laser atherectomy devices are significantly driving market growth. Innovations in laser delivery systems, such as fibre-optic technology and improved energy modulation, have enhanced the precision, safety, and efficacy of these devices. Modern systems now offer imaging-guided capabilities, allowing for real-time visualization of arterial blockages, which helps in accurately targeting and removing lesions. Additionally, the development of portable and user-friendly devices has expanded their applicability to outpatient and ambulatory care settings, improving accessibility for patients. Manufacturers are also focusing on integrating AI and machine learning algorithms to optimize procedure outcomes and reduce operator dependency. These technological improvements address limitations of traditional methods, such as high complication rates or difficulty in treating calcified lesions, making laser atherectomy devices a reliable choice for vascular interventions. Such advancements continue to attract both healthcare providers and patients, bolstering market growth globally.

Restraint Factor for the Laser Atherectomy D...