Dataset Description: ECG Data Categorized by Cardiac Condition

This dataset comprises electrocardiogram (ECG) data organized into three distinct categories based on patient cardiac health and dataset collected by the National Heart Foundation Bangladesh (NHFB) from June 2023 to December 2023.

1. Arrhythmia Patients: This category contains ECG data from individuals diagnosed with cardiac arrhythmias, characterized by irregular heart rhythms. The data within this category may encompass various types of arrhythmias, requiring further sub-classification depending on the specific research objectives.

2. Myocardial Patients: This category encompasses ECG data from patients experiencing myocardial issues, most likely referring to myocardial infarction (heart attack) or other diseases affecting the myocardium (heart muscle). The specific myocardial conditions represented within this category may require further specification depending on the dataset's scope and purpose.

3. Normal Patients: This category serves as a control group and includes ECG data from individuals deemed to have healthy cardiac function. These individuals exhibit no clinically significant ECG abnormalities or diagnosed cardiac conditions.

Dataset Structure:

The dataset is structured into three folders, each corresponding to a specific patient category: "Arrhythmia Patient," "Myocardial Patient," and "Normal Patient." .

Potential Applications:

This dataset can be utilized for various research and educational purposes, including:

• Developing and evaluating algorithms for automated arrhythmia detection and classification.

• Investigating the ECG characteristics associated with different myocardial conditions.

• Training machine learning models for cardiac disease diagnosis and risk stratification.

• Educating students and healthcare professionals on ECG interpretation and cardiac pathologies.

Further Information:

Detailed information regarding the data acquisition protocol, ECG recording parameters, patient demographics, and data annotation procedures is essential for comprehensive dataset utilization. Accessing relevant documentation accompanying the dataset is crucial for ensuring appropriate data interpretation and analysis.

Source: https://physionet.org/content/ptb-xl/1.0.1/

Abstract

Electrocardiography (ECG) is a key diagnostic tool to assess the cardiac condition of a patient. Automatic ECG interpretation algorithms as diagnosis support systems promise large reliefs for the medical personnel - only on the basis of the number of ECGs that are routinely taken. However, the development of such algorithms requires large training datasets and clear benchmark procedures. In our opinion, both aspects are not covered satisfactorily by existing freely accessible ECG datasets.

The PTB-XL ECG dataset is a large dataset of 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The raw waveform data was annotated by up to two cardiologists, who assigned potentially multiple ECG statements to each record. The in total 71 different ECG statements conform to the SCP-ECG standard and cover diagnostic, form, and rhythm statements. To ensure comparability of machine learning algorithms trained on the dataset, we provide recommended splits into training and test sets. In combination with the extensive annotation, this turns the dataset into a rich resource for the training and the evaluation of automatic ECG interpretation algorithms. The dataset is complemented by extensive metadata on demographics, infarction characteristics, likelihoods for diagnostic ECG statements as well as annotated signal properties.

Background

The waveform data underlying the PTB-XL ECG dataset was collected with devices from Schiller AG over the course of nearly seven years between October 1989 and June 1996. With the acquisition of the original database from Schiller AG, the full usage rights were transferred to the PTB. The records were curated and converted into a structured database within a long-term project at the Physikalisch-Technische Bundesanstalt (PTB). The database was used in a number of publications, see e.g. [1,2], but the access remained restricted until now. The Institutional Ethics Committee approved the publication of the anonymous data in an open-access database (PTB-2020-1). During the public release process in 2019, the existing database was streamlined with particular regard to usability and accessibility for the machine learning community. Waveform and metadata were converted to open data formats that can easily processed by standard software.

Methods

Data Acquisition

1. Raw signal data was recorded and stored in a proprietary compressed format. For all signals, we provide the standard set of 12 leads (I, II, III, AVL, AVR, AVF, V1, ..., V6) with reference electrodes on the right arm.
2. The corresponding general metadata (such as age, sex, weight and height) was collected in a database.
3. Each record was annotated with a report string (generated by cardiologist or automatic interpretation by ECG-device) which was converted into a standardized set of SCP-ECG statements (scp_codes). For most records also the heart’s axis (heart_axis) and infarction stadium (infarction_stadium1 and infarction_stadium2, if present) were extracted.
4. A large fraction of the records was validated by a second cardiologist.
5. All records were validated by a technical expert focusing mainly on signal characteristics.

Data Preprocessing

ECGs and patients are identified by unique identifiers (ecg_id and patient_id). Personal information in the metadata, such as names of validating cardiologists, nurses and recording site (hospital etc.) of the recording was pseudonymized. The date of birth only as age at the time of the ECG recording, where ages of more than 89 years appear in the range of 300 years in compliance with HIPAA standards. Furthermore, all ECG recording dates were shifted by a random offset for each patient. The ECG statements used for annotating the records follow the SCP-ECG standard [3].

Data Description

In general, the dataset is organized as follows: ptbxl ├── ptbxl_database.csv ├── scp_statements.csv ├── records100 ├── 00000 │ │ ├── 00001_lr.dat │ │ ├── 00001_lr.hea │ │ ├── ... │ │ ├── 00999_lr.dat │ │ └── 00999_lr.hea │ ├── ... │ └── 21000 │ ├── 21001_lr.dat │ ├── 21001_lr.hea │ ├── ... │ ├── 21837_lr.dat │ └── 21837_lr.hea └── records500 ├── 00000 │ ├── 00001_hr.dat │ ├── 00001_hr.hea │ ├── ... │ ├── 00999_hr.dat │ └── 00999_hr.hea ├── ... └── 21000 ├── 21001_hr.dat ├── 21001_hr.hea ├── ... ├── 21837_hr.dat └── 21837_hr.hea The dataset comprises 21837 clinical 12-lead ECG records of 10 seconds length from 18885 patients, where 52% are male and 48% are female with ages covering the whole range from 0 to 95 years (median 62 and interquantile range of 22). The value of the dataset results from the comprehensive collection of many different co-occurring path...

Electrocardiograph (ECG) Analysis Software Market Outlook

The global Electrocardiograph (ECG) Analysis Software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 3.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.5% during the forecast period. This growth is largely driven by the increasing prevalence of cardiovascular diseases, advancements in healthcare IT, and the growing demand for precise and efficient diagnostic tools.

The rising incidence of cardiovascular diseases (CVDs) globally is a significant growth factor for the ECG analysis software market. As CVDs continue to be the leading cause of mortality worldwide, there is an increasing need for accurate and early diagnosis. ECG analysis software provides critical insights that can aid in the timely detection and management of heart conditions, thereby enhancing patient outcomes. The aging population further exacerbates the prevalence of cardiovascular issues, necessitating advanced diagnostic tools and driving the demand for sophisticated ECG analysis software.

Technological advancements in healthcare IT and the integration of artificial intelligence (AI) and machine learning (ML) into ECG analysis are other crucial factors propelling market growth. AI and ML algorithms can analyze ECG data with high precision, identify patterns, and predict potential heart conditions, thus facilitating more accurate diagnoses. The advent of wearable technology and mobile health applications has also expanded the accessibility and utility of ECG analysis software, enabling continuous monitoring and real-time data collection and analysis.

The growing emphasis on personalized medicine is another driving force behind the market's expansion. Personalized medicine focuses on tailoring medical treatment to individual patient characteristics, and ECG analysis software plays a pivotal role in this approach by providing detailed and patient-specific cardiac data. This allows healthcare providers to develop customized treatment plans, improving patient care and outcomes. Additionally, the increasing adoption of telemedicine, especially highlighted by the COVID-19 pandemic, has further accelerated the demand for remote ECG monitoring and analysis solutions.

Regionally, North America is expected to dominate the ECG analysis software market due to the presence of a well-established healthcare infrastructure, high adoption of advanced technologies, and significant investments in healthcare IT. Europe follows closely, driven by favorable government initiatives and a strong focus on research and development. The Asia Pacific region is anticipated to experience the highest growth rate, owing to the rising prevalence of cardiovascular diseases, improving healthcare infrastructure, and increasing healthcare expenditure in emerging economies such as China and India.

Product Type Analysis

The ECG analysis software market is segmented into standalone ECG software and integrated ECG software. Standalone ECG software operates independently and is typically used in settings where there may be a need for specialized analysis of ECG data without the integration into broader healthcare systems. This type of software is often favored by smaller clinics and diagnostic centers due to its cost-effectiveness and ease of use. Standalone systems provide robust ECG analysis capabilities and are designed to offer detailed insights into cardiac health.

Integrated ECG software, on the other hand, is designed to be part of a comprehensive healthcare management system. This integration allows for seamless data transfer, improved workflow efficiency, and better patient management. Integrated systems are increasingly becoming popular in larger healthcare settings such as hospitals and multi-specialty clinics. These systems facilitate the automatic updating of patient records and ensure that all relevant healthcare providers have access to comprehensive and up-to-date patient information.

The choice between standalone and integrated ECG software often depends on the specific needs and resources of the healthcare facility. While standalone systems might be easier to implement and operate on a smaller scale, integrated systems provide greater efficiency and data management capabilities in larger, more complex healthcare environments. Each segment is poised for growth, with standalone systems catering to the needs of smaller practices and integrated systems driving adoption in larger institutions.

Moreover, the increa

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.

Rationale

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.

Subjects and data acquisition protocol

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 subject_info.xlsx.

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.

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.

Technical details

The acquired signals are provided in MAT-files named as follows:

XX_YYY,

where XX is the patient ID, YYY 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.

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.

In the subject_info.xlsx 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.

Limitations

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.

This data set divided in two sub categories: Inertial sensors data and ECG data. Inertial data comprises body motion recordings for several volunteers of diverse profile while performing certain physical activities. Sensors placed on the subject's waist is used to measure the motion experienced by diverse body parts, namely, acceleration and rate of turn. Data is divided into five age and weight groups categories. ECG data collection was collected using Android smartphone application, and data was saved in common separate vector (csv) file format. The ECG data acquired according to the Limb Lead I and limb Lead II configuration

The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.

Please refer to: https://www.ncbi.nlm.nih.gov/pubmed/24377903

Comparative analysis of ECG feature values and their standard deviation.

ECG Monitor Market Outlook

The global ECG Monitor market has shown significant potential for growth, with a market size valued at approximately $6.5 billion in 2023 and projected to reach around $10.5 billion by 2032, reflecting a promising CAGR of 5.5% over the forecast period. This growth is primarily driven by the increasing prevalence of cardiovascular diseases globally, which has heightened the demand for accessible and efficient cardiac monitoring solutions. Technological advancements in ECG monitors, improved healthcare infrastructure, and heightened awareness regarding early diagnosis of heart conditions further bolster this market's expansion.

One of the main growth factors for the ECG Monitor market is the rising incidence of cardiovascular diseases worldwide. Heart diseases remain the leading cause of death globally, prompting a greater demand for continuous and real-time cardiac monitoring. As lifestyles become more sedentary and populations age, the prevalence of heart-related ailments is expected to rise, thus driving the need for advanced ECG monitoring solutions. Moreover, the integration of innovative features such as wireless connectivity, portability, and user-friendly interfaces in ECG devices enhances their practicality and adoption in various healthcare settings.

Advancements in healthcare technology significantly contribute to the growth of the ECG Monitor market. The transition from traditional ECG devices to smart, connected systems enables seamless data collection and transfer, improving the efficiency of patient care. These technological strides include the development of compact and portable ECG monitors that can be easily used in home settings, allowing for better patient compliance and continuous monitoring. Additionally, the integration of artificial intelligence and machine learning algorithms into these devices facilitates the early detection of arrhythmias and other cardiac anomalies, thereby enhancing diagnostic accuracy.

Government initiatives and healthcare reforms also play a crucial role in driving the ECG Monitor market. Many countries are investing in healthcare infrastructure improvements, which include the adoption of advanced medical devices like ECG monitors to enhance patient care and reduce the burden of cardiovascular diseases. Furthermore, increasing healthcare spending in emerging economies, along with favorable reimbursement policies, is expected to provide a significant boost to the market. This is particularly true in regions where there is a high burden of lifestyle-related heart diseases, necessitating the need for efficient monitoring solutions.

From a regional perspective, North America currently leads the ECG Monitor market due to its advanced healthcare system, high prevalence of cardiovascular diseases, and proactive healthcare policies. However, the Asia Pacific region is anticipated to witness the fastest growth, driven by factors such as rising healthcare awareness, increased medical tourism, and expanding healthcare investments. The growing middle-class population and improved access to healthcare facilities further support the market's growth in this region. Additionally, Europe remains a significant player due to its well-established healthcare infrastructure and increasing demand for technologically advanced medical devices.

Product Type Analysis

The ECG Monitor market is segmented into several product types, including Resting ECG, Stress ECG, Holter Monitors, Event Monitors, and others. Each of these product types caters to specific cardiac monitoring needs. Resting ECG devices are commonly used in clinical settings to provide a baseline reading of heart activity, making them a staple in hospitals and clinics. Their reliability and ease of use make them a preferred choice for initial cardiac assessments. The demand for Resting ECGs is expected to remain steady, supported by the continuous incidence of heart disease and the need for regular cardiac evaluations.

Stress ECG monitors are integral in diagnosing heart conditions that manifest under physical exertion. These devices are vital in assessing how the heart functions under stress, particularly in patients with suspected coronary artery disease. The increasing awareness of cardiovascular health and the rise in fitness-related evaluations are driving the demand for Stress ECG monitors. Furthermore, advancements in technology have made these devices more accessible and efficient, allowing for more widespread adoption across various healthcare settings, including specialized cardiac centers and sports medicine practices.

AbstractThe NCKU CBIC ECG database collects ECG data from 6 different patients. The patients information have been processed for anonymization, and each patient has signed a consent form to ensure the legitimacy of data usage. Each patient collects lead II ECG for four hours a day to highlight patients' different physiological meanings at different times of the day, and the database provides the labels for motion artifact and baseline wandering, which are invalid signal for diagnosis. Prevent physicians from using the noise signal to diagnose. These data were collected using Patch[1] at Ministry of Health and Welfare Tainan Hospital, and the included data have been approved by the Institutional Review Board (IRB).BackgroundTechnology and medical treatment are highly developed in the 21st century, and people have more irregular daily routines and greater life pressure. Cardiovascular disease has become a tough nut to crack when the changing of lifestyle is coupled with the aging of society. The age distribution of patients is wider than ever. A wealth of health information can be obtained through electrocardiogram (ECG) measurement, including cardiac arrhythmias. Severe arrhythmias will lead to many life problems, including palpitations, chest tightness, dizziness, shock, and even life-threatening conditions. Therefore, the monitoring of ECG signal is quite essential.To do our part in the study of arrhythmia, our team started the patient enrollment after gaining the permission of the National Cheng Kung University Hospital Institutional Review Board (NCKUH IRB No. B-ER-104-379) from 2018. We have selected total 128 patients' 24 hours ECG data until now. The results of the arrhythmia label are confirmed by the cardiologist Ju-Yi Chen in NCKUH. Finally, We selected 6 patients from the received signals and made them into a database for researchers to access.MethodsThe NCKU CBIC ECG database contains the ECG recordings from 6 subjects. The signals were collected in Tainan Hospital (Ministry of Health and Welfare) via an ECG acquisition device[1] developed by Your health technology Co., Ltd. The sampling frequency is 400Hz, and the ADC resolution is 12 bits.The age distribution of subjects was from 24 to 76 years old, and each patient was measured at the lead II for 24 hours. After the signal is recorded, four cleaner segments in the morning, noon, evening, and midnight are selected, and each segment is one hour long. The heartbeat of human body is different when sleeping and awake, and some arrhythmia type occurs at sleeping period often. It's hard to detect some arrhythmia at specific time of a day, therefore, we choose signal segments from different time period for a patient, which is more representative of the daily heartbeat condition. It's worth mentioning that the ECG signals from the 6th subject contains too many noise signals in the daytime due to his career type, so the segments from 22:00 to 02:00 are selected.We have collected total 128 patients from Tainan Hospital since 2018. Since most of the ECG data of patients are normal beats, we finally selected the ECG data of six patients which contain clinically significant arrhythmia. The database provides two particular label type for motion artifact and baseline wandering, which are caused by body movement during ECG acquisition. In actual situations, cardiologist doesn't use the noise signals as a basis for diagnosis, therefore, these two specific labels prevent physicians from using noise to make a diagnosis.The original data is first compared with the holter report, and the R peak position and beat labels are manually marked. And then the data were given to a professional cardiologist, Ju-Yi, Chen, for verification. The cardiologist checked the correction and position of beat labels, and chose the acceptable signal segmentation for high quality.Introduction of Ju-Yi, Chen :JU-YI CHEN was born in Tainan, Taiwan, in 1974. He received the M.S. degree from Chang Gung University, Taoyuan City, Taiwan, in 1999 and the Ph.D. degree from the National Cheng Kung University, Tainan, in 2013. Since 2021, he has been a Professor at the Department of Internal Medicine, National Cheng Kung University. His current research interests include the cardiovascular diseases, including arrhythmias, hypertension, arterial stiffness, and cardiac implantable electric devices.Data DescriptionThe file structure and naming rule are described as follows :[The subject number]_[The measurement time] : The directory nameOUTPUT_ECG_data.csv : The one-hour ECG signals ( unit : 0.1V )OUTPUT_peak_label.csv : The arrhythmia type label of R-peakOUTPUT_peak_position.csv : The position of R-peakex : 1_0100 directory contains subject No. 1's data which is measured at 01:00.Arrhythmia diseases and the corresponding label codes :Code Arrhythmia Disease—————————————————————0 Normal1 Atrial Fibrillation2 Supraventricular Tachycardia3 Premature Ventricular Contraction4 Atrial Premature Contraction5 Motion Artifact6 Wandering7 First degree AV block8 Atrial FlutterPS : Wandering represents baseline drifted by 1mV.Patient information :Subject 1: Male，61 yearsSubject 2: Female，77 yearsSubject 3: Male，63 yearsSubject 4: Male，64 yearsSubject 5: Male，24 yearsSubject 6: Male，64 yearsUsage NotesFew public ECG databases provide long-term ECG, our goal in creating the database is to help understand what a person's ECG looks like in a day, and this database is more valuable in obtaining long-term ECG.EthicsOur team has cooperated with National Cheng Kung University Hospital and Tainan Hospital. All the patients enrolled gave their informed consent to participate in the study. The certification of safety-related IEC standards and human study approval are all acquired.Conflicts of InterestThe authors declare that there are no known conflicts of interest.ReferencesS.-Y. Lee, P.-W. Huang, M.-C. Liang, J.-H. Hong, and J.-Y. Chen, "Development of an arrhythmia monitoring system and human study," IEEE Transactions on Consumer Electronics, vol. 64, no. 4, pp. 442-451, 2018.

According to Cognitive Market Research, the global ECG Monitoring Equipment Market size will be USD 6124.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 2449.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 4873.56 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1408.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 306.23 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 122.49 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2024 to 2031.
The Resting ECG Systems Segment held the highest ECG Monitoring Equipment market revenue share in 2024.

Market Dynamics of ECG Monitoring Equipment Market

Key Drivers for ECG Monitoring Equipment Market

Rising Awareness and Early Diagnosis of Heart Conditions

The rising awareness and emphasis on early diagnosis of heart conditions are driving growth in the ECG monitoring equipment market. Increased public knowledge about cardiovascular health, bolstered by educational campaigns and health initiatives, has led to a proactive approach in detecting heart diseases before they become severe. This growing awareness is encouraging more frequent screenings and preventative measures, which in turn fuels demand for ECG monitoring equipment. Early diagnosis improves patient outcomes and reduces overall healthcare costs, making advanced ECG systems a critical component in modern healthcare. As individuals and healthcare providers focus more on early intervention, the market for ECG monitoring equipment is expected to expand significantly.

Increasing Demand for Portable and Wearable ECG Devices

The increasing demand for portable and wearable ECG devices is reshaping the ECG monitoring equipment market. As healthcare moves towards more personalized and accessible solutions, there is a growing preference for devices that allow continuous monitoring outside traditional clinical settings. Portable and wearable ECG devices offer the advantage of real-time data collection and ease of use, catering to patients who need ongoing heart monitoring without the constraints of hospital visits. These devices are especially popular among individuals with chronic conditions or those at high risk of cardiovascular issues. The shift towards remote and on-the-go health monitoring is driving innovation and expansion in the portable ECG device segment, aligning with broader trends in healthcare technology and patient management.?

Restraint Factor for the ECG Monitoring Equipment Market

Limited availability of skilled professionals for operation and interpretation.

The limited availability of skilled professionals for the operation and interpretation of ECG monitoring equipment is a significant restraint in the market. Accurate operation and analysis of ECG data require specialized training and expertise, which can be scarce in certain regions or healthcare settings. This shortage can lead to underutilization of advanced ECG systems and delays in diagnosis, impacting patient care and operational efficiency. Healthcare facilities may face challenges in recruiting and retaining qualified staff, which exacerbates the issue. Additionally, the need for ongoing training and education to keep up with evolving technology adds to the burden. Addressing this skill gap is crucial for optimizing the use of ECG monitoring equipment and ensuring accurate, timely heart disease management.

Impact of Covid-19 on the ECG Monitoring Equipment Market

The COVID-19 pandemic had a multifaceted impact on the ECG monitoring equipment market. On one hand, the increased demand for remote and continuous monitoring solutions surged due to the need for monitoring patients with cardiovascular conditions without frequent hospital visits. This boosted the adoption of portable and wearable E...

This newly inaugurated research database for 12-lead electrocardiogram (ECG) signals was created under the auspices of Chapman University, Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine), and Ningbo First Hospital. It aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, ECG is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable time and effort by human experts. Modern machine learning and statistical tools can be trained on high quality, large data to achieve exceptional levels of automated diagnostic accuracy. Thus, we collected and disseminated this novel database that contains 12-lead ECGs of 45,152 patients with a 500 Hz sampling rate that features multiple common rhythms and additional cardiovascular conditions, all labeled by professional experts. The dataset can be used to design, compare, and fine-tune new and classical statistical and machine learning techniques in studies focused on arrhythmia and other cardiovascular conditions.

The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions.

PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below:

• metadata.csv:
  This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns:
  • Image name: image name with extension,
  • ECG ID: this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the leads.npz and rhythms.npz,
  • Image relative path: relative path to the image in question,
  • Image page: page number of the particular image (starting from 0),
  • ECG number of pages: number of pages in the whole ECG,
  • ECG number of columns per page: number of columns per page in the ECG,
  • ECG number of rows per page: number of rows in the ECG,
  • ECG number of rhythm leads: number of rhythms in the ECG,
  • ECG format: short version of the ECG format.
• data folder:
  • leads.npz: NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.
  • rhythms.npz: NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.
• visual_data folder:
  This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The metadata.csv file provides a direct link to each image and its associated ECG information.

Subjects for whom exercise represents a low risk level, based on standardized guidelines from the American College of Sports Medicine (ACSM) [20], were asked to participate in the study. Eighteen healthy subjects, 11 males and 7 females, age 21 ± 3 years were enrolled. Participants were asked to avoid caffeine and alcohol during the 48 hours preceding the test, and were instructed to fast (water only) for at least 3 h before testing. The study was conducted in a quiet, comfortable room (ambient temperature, 18-20 °C, and relative humidity between 30-50%). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. This protocol was approved by the Institutional Review Board of the University of Connecticut.

Before the exercise test began, the subjects were asked to lay in the supine position for 5 min to procure hemodynamic stabilization prior to 5 minutes of data collection in this position. ECG and EDA were measured simultaneously for each subject throughout the entire experiment. The ECG signal was used to monitor subjects’ HR throughout the experiment. An HP ECG monitor (HP 78354A) and GSR ADInstruments module were used. Three hydrogel Ag-AgCl electrodes were used for ECG signal collection. The electrodes were placed on the shoulders and lower left rib. In addition, a pair of stainless steel electrodes were placed on index and middle fingers of the right hand to collect the EDA signal. Subjects were instructed to keep their right hand stable, raised at chest height. The skin was cleaned with alcohol before placing the ECG and EDA electrodes. The leads were taped to the subject’s skin using latex-free tape, to avoid movement of the cables, which can corrupt the signals. All signals were acquired through the ADInstruments analog-to-digital converter, and compatible PowerLab software, while the sampling frequency was fixed to 400 Hz for all signals. Participants were asked to wear their own active wear/gym clothes during the protocol with the shirt covering the electrodes and cables during the experiment.Subjects were first monitored for 5 min at rest (supine, without any movement or talking) to measure resting HR and EDA. The subjects then performed the incremental test on a motorized treadmill (Life Fitness F3). 85% HRmax was calculated from the equation HRmax = 206.9-(0.67*age).The incremental running began with an initial warm-up, followed by walking at 3mi/h (~ 4.82 km/h). The speed was increased to 5 mi/h (~ 8 km/h) and increased 0.6 mi/h (about 1 km/h) every subsequent minute until the subjects reached 85% of their HRmax. When a subject reached 85% of HRmax within 2 min of running, the data were excluded because at least 2 minutes of data are required for processing. The 18 subjects enrolled for this study represents those who were able to provide at least 2 minutes of data prior to reaching 85% of HRmax. After subjects reached 85% of their HRmax, treadmill speed was reduced to 5 mi/h (~ 8 km/h) for another 4 min to start the recovery phase, followed by walking at 3 mi/h (about 4.82 km/h) for 5 minutes. A final 10 min period (or more if needed to achieve baseline HR) in the supine position was utilized to allow HR to return to baseline. The duration of the experiment was approximately one hour.

The global digital ambulatory ECG recorder market is experiencing robust growth, driven by a confluence of factors. Technological advancements leading to smaller, more user-friendly devices, coupled with increasing awareness of cardiovascular diseases and the rising prevalence of cardiac arrhythmias, are significantly boosting market demand. The integration of advanced analytics and remote monitoring capabilities within these recorders enhances diagnostic accuracy and facilitates timely interventions, further propelling market expansion. The market is segmented by application (Mechanical Engineering, Automotive, Aeronautics, Marine, Oil & Gas, Chemical Industrial, Medical, Electrical) and type (Micro, Medium, Others), with the medical application segment dominating due to widespread use in patient monitoring and diagnosis. North America currently holds a substantial market share, attributed to high healthcare expenditure and advanced medical infrastructure. However, Asia Pacific is projected to witness the fastest growth rate over the forecast period (2025-2033), driven by expanding healthcare access and increasing adoption of advanced diagnostic technologies in developing economies like India and China. While the high initial cost of equipment and potential data security concerns present some restraints, the overall market outlook remains positive, indicating a substantial increase in market size and revenue over the coming years. Competition among established players like GE Healthcare, Hillrom (Baxter), and others is driving innovation and creating more sophisticated products. Further growth is expected from the increasing adoption of telehealth and remote patient monitoring programs. This trend minimizes the need for frequent hospital visits, which is particularly beneficial for patients with chronic heart conditions. The ongoing miniaturization of ECG recorders is making them more comfortable and convenient for patients to use, leading to greater compliance and improved data collection. Future growth will also be influenced by regulatory changes and reimbursement policies in different regions. As technology continues to improve, and data analysis becomes more sophisticated, we anticipate a continued shift toward more integrated and comprehensive cardiac monitoring solutions, further expanding the market for digital ambulatory ECG recorders. The market is ripe for innovation in areas such as AI-powered diagnostics and improved data security, promising even more significant expansion in the years to come.

Electrocardiography (ECG) Monitoring Market Outlook

The global Electrocardiography (ECG) Monitoring market size is expected to grow significantly, with a projected CAGR of 6.5% from 2024 to 2032. In 2023, the market was valued at approximately USD 5.9 billion, and it is forecasted to reach around USD 10.3 billion by 2032. The market growth is primarily driven by the increasing prevalence of cardiovascular diseases, technological advancements in ECG devices, and the rising geriatric population.

One of the primary growth factors of the ECG Monitoring market is the global increase in cardiovascular diseases (CVDs). With heart disease being the leading cause of death worldwide, there is a growing need for effective, early detection, and continuous monitoring of heart conditions. This demand is driving the adoption of advanced ECG monitoring devices that provide accurate, real-time data, enhancing patient outcomes. Additionally, a sedentary lifestyle, poor dietary habits, and increasing stress levels contribute to the rise in cardiovascular diseases, further boosting the market.

Technological advancements in ECG monitoring devices are also a crucial factor propelling market growth. Modern ECG devices are becoming more compact, user-friendly, and integrated with advanced features such as wireless connectivity, cloud storage, and AI-driven data analytics. These innovations enhance the accuracy and accessibility of ECG monitoring, making it easier for healthcare providers to diagnose and manage cardiac conditions. The integration of mobile health (mHealth) technologies and telemedicine platforms has also expanded the reach and convenience of ECG monitoring, particularly in remote and underserved regions.

The rising geriatric population is another significant driver of the ECG Monitoring market. Older adults are more susceptible to heart diseases, and the demand for continuous and efficient cardiac monitoring solutions is higher in this demographic. With increasing life expectancy and an aging global population, the need for reliable ECG monitoring devices is expected to grow substantially. Furthermore, the shift towards home-based healthcare and patient-centric approaches is encouraging the adoption of portable and wearable ECG devices, providing convenience and timely interventions for elderly patients.

The integration of ECG Equipment & Management System has revolutionized cardiac care by streamlining the process of monitoring and managing heart health. These systems offer comprehensive solutions that combine hardware and software to provide seamless data collection, analysis, and storage. By enabling real-time access to patient data, healthcare providers can make informed decisions quickly, enhancing the quality of care. The adoption of these systems is particularly beneficial in large healthcare facilities where managing vast amounts of patient data efficiently is crucial. Moreover, the ability to integrate with other hospital information systems ensures a holistic approach to patient management, reducing the risk of data errors and improving patient outcomes.

Regionally, North America holds the dominant position in the ECG Monitoring market, attributed to the high prevalence of cardiovascular diseases, advanced healthcare infrastructure, and significant investments in healthcare technologies. Europe follows closely, driven by similar factors and supportive government initiatives promoting cardiac health. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, fueled by an increasing patient pool, rising healthcare expenditure, and growing awareness about heart health and diagnostic technologies.

Product Type Analysis

The ECG Monitoring market by product type is segmented into Resting ECG, Stress ECG, Holter Monitors, Event Monitors, Mobile Cardiac Telemetry Devices, and Implantable Loop Recorders. Each of these product types serves specific diagnostic and monitoring needs, catering to varying patient conditions and healthcare settings.

Resting ECG devices are fundamental tools in cardiology, used primarily for initial diagnosis of heart conditions. These devices record the heart's electrical activity when the patient is at rest, providing critical baseline data for diagnosing arrhythmias, myocardial infarctions, and other cardiac abnormalities. The simplicity and cost-effectiveness of Resting ECG devices make them widely used in hospitals, c

The ECGs in this collection were obtained using a non-commercial, PTB prototype recorder with the following specifications:

16 input channels, (14 for ECGs, 1 for respiration, 1 for line voltage) Input voltage: ±16 mV, compensated offset voltage up to ± 300 mV Input resistance: 100 Ω (DC) Resolution: 16 bit with 0.5 μV/LSB (2000 A/D units per mV) Bandwidth: 0 - 1 kHz (synchronous sampling of all channels) Noise voltage: max. 10 μV (pp), respectively 3 μV (RMS) with input short circuit Online recording of skin resistance Noise level recording during signal collection The database contains 549 records from 290 subjects (aged 17 to 87, mean 57.2; 209 men, mean age 55.5, and 81 women, mean age 61.6; ages were not recorded for 1 female and 14 male subjects). Each subject is represented by one to five records. There are no subjects numbered 124, 132, 134, or 161. Each record includes 15 simultaneously measured signals: the conventional 12 leads (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6) together with the 3 Frank lead ECGs (vx, vy, vz). Each signal is digitized at 1000 samples per second, with 16 bit resolution over a range of ± 16.384 mV. On special request to the contributors of the database, recordings may be available at sampling rates up to 10 KHz.

Within the header (.hea) file of most of these ECG records is a detailed clinical summary, including age, gender, diagnosis, and where applicable, data on medical history, medication and interventions, coronary artery pathology, ventriculography, echocardiography, and hemodynamics. The clinical summary is not available for 22 subjects.

Self-Powered ECG Necklace Market Outlook

According to our latest research, the global market size for Self-Powered ECG Necklaces reached USD 412 million in 2024, with a robust compound annual growth rate (CAGR) of 18.7% projected from 2025 to 2033. This trajectory is expected to propel the market to a forecasted value of USD 2.22 billion by 2033. The primary growth factor fueling this market is the increasing demand for continuous, non-invasive cardiac monitoring solutions, driven by rising cardiovascular disease prevalence and the growing adoption of wearable health technologies worldwide.

The Self-Powered ECG Necklace Market is witnessing significant momentum due to the convergence of healthcare digitization and advancements in energy harvesting technologies. Traditional ECG monitoring devices often present limitations in portability and require frequent charging or battery replacement, which can hinder long-term patient compliance. Self-powered ECG necklaces overcome these challenges by leveraging piezoelectric, triboelectric, and thermoelectric technologies to generate their own energy from body motion or heat, thereby enabling uninterrupted health monitoring. This innovation is particularly crucial for elderly populations and patients with chronic cardiac conditions, as it reduces the risk of missed arrhythmia episodes and enhances early detection of cardiac events. Moreover, the integration of wireless data transmission and cloud-based analytics further amplifies the value proposition of these devices, making them indispensable in modern healthcare ecosystems.

Another major growth driver is the expanding consumer focus on preventive healthcare and personal wellness. The proliferation of fitness culture, coupled with increasing health awareness post-pandemic, has resulted in a surge in demand for wearable devices that offer real-time biometrics tracking, including heart rate variability and ECG signals. Self-powered ECG necklaces are uniquely positioned to address this demand, as they offer a seamless blend of medical-grade monitoring and user convenience without the hassle of regular charging. Additionally, the growing trend of remote patient monitoring, supported by favorable regulatory frameworks and reimbursement policies in several regions, is accelerating the adoption of these wearable devices in both clinical and non-clinical settings. This, in turn, is fostering innovation among manufacturers to develop more compact, aesthetically pleasing, and user-friendly necklace designs.

The market is also benefiting from strategic collaborations between technology providers, healthcare institutions, and insurance companies. These partnerships are facilitating the integration of self-powered ECG necklaces into broader telemedicine and digital health platforms, enabling continuous patient data collection and real-time physician feedback. As artificial intelligence and machine learning algorithms become increasingly sophisticated, the diagnostic accuracy and predictive capabilities of these devices are expected to improve further, driving higher adoption rates. However, market players must navigate challenges such as data privacy concerns, device standardization, and interoperability with existing health infrastructure to unlock the full potential of this technology.

Regionally, North America currently dominates the Self-Powered ECG Necklace Market, accounting for the largest share in 2024, owing to its advanced healthcare infrastructure, high adoption of digital health solutions, and supportive regulatory environment. However, Asia Pacific is projected to exhibit the fastest growth over the forecast period, fueled by rising healthcare expenditures, a burgeoning middle-class population, and increased government initiatives to promote preventive healthcare. Europe also represents a significant market, driven by the increasing prevalence of cardiovascular diseases and a strong focus on technological innovation. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets due to improving healthcare access and growing awareness of wearable health technologies.

Portable ECG Devices Market Outlook

The global portable ECG devices market size was valued at approximately USD 2.5 billion in 2023 and is expected to grow to around USD 5.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 9.7% during the forecast period. This growth is primarily driven by the increasing prevalence of cardiovascular diseases, the rising awareness about early diagnosis, and technological advancements in portable medical devices.

One of the key growth factors for the portable ECG devices market is the rising prevalence of cardiovascular diseases globally. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death worldwide, taking an estimated 17.9 million lives each year. This alarming statistic underscores the need for efficient and accessible diagnostic tools, such as portable ECG devices, which can help in the early detection and management of heart conditions. The convenience and ease of use offered by these devices make them particularly attractive to both healthcare providers and patients.

Technological advancements also play a crucial role in the growth of the portable ECG devices market. Innovations such as wireless connectivity, integration with smartphones, and cloud-based data storage have made these devices more user-friendly and efficient. These advancements not only enhance the functionality of the devices but also make them more accessible to a broader population, including those in remote and rural areas. Additionally, the incorporation of artificial intelligence (AI) and machine learning (ML) algorithms in these devices has improved the accuracy and reliability of ECG readings, further boosting their adoption.

The emergence of the Mobile Electrocardiograph Monitor has further revolutionized the landscape of portable ECG devices. These monitors are designed to provide real-time heart monitoring with the convenience of mobility, allowing patients to carry them wherever they go. The integration of mobile technology with ECG monitoring enables continuous data collection and transmission to healthcare providers, facilitating timely diagnosis and intervention. This is particularly beneficial for patients with chronic heart conditions who require constant monitoring. The ability to track heart health on-the-go not only enhances patient convenience but also empowers individuals to take proactive steps in managing their cardiovascular health. As technology continues to advance, the Mobile Electrocardiograph Monitor is expected to become an integral part of personalized healthcare solutions, offering a seamless blend of technology and medical care.

The increasing focus on preventive healthcare is another significant factor driving the market. As healthcare costs continue to rise, there is a growing emphasis on early diagnosis and preventive measures to manage chronic diseases more effectively. Portable ECG devices offer a convenient and cost-effective solution for continuous monitoring of heart health, enabling timely intervention and reducing the risk of severe complications. This shift towards preventive healthcare is expected to fuel the demand for portable ECG devices in the coming years.

Regionally, North America holds a dominant position in the portable ECG devices market, accounting for a significant share of the global market. This can be attributed to the high prevalence of cardiovascular diseases, advanced healthcare infrastructure, and increased healthcare spending in the region. Moreover, the presence of key market players and favorable reimbursement policies further support market growth. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rising healthcare awareness, increasing disposable income, and growing adoption of advanced medical technologies.

Product Type Analysis

The portable ECG devices market is segmented into various product types, including handheld, pen, wearable, and others. Handheld ECG devices are among the most commonly used types, owing to their ease of use and portability. These devices are particularly popular among patients who need to monitor their heart health regularly but prefer to do so in the comfort of their homes. The handheld segment is expected to maintain its dominance during the forecast period, driven by the increasing demand for user-friendly and reliable devices.

Pen-type ECG devi

The global wearable ECG device market, valued at $19,890 million in 2019, is projected to experience robust growth, driven by several key factors. The increasing prevalence of cardiovascular diseases, coupled with the rising demand for convenient and accessible health monitoring solutions, fuels market expansion. Technological advancements leading to smaller, more accurate, and user-friendly devices are further contributing to market growth. Furthermore, the integration of wearable ECG devices with smartphone apps and cloud-based platforms enables remote patient monitoring and timely interventions, enhancing their appeal among healthcare providers and consumers. The competitive landscape includes established players like Apple, Philips, and GE Healthcare, alongside innovative startups. These companies are constantly striving to improve device features, such as longer battery life, enhanced data analytics capabilities, and improved integration with existing healthcare infrastructure. The market is also witnessing a shift towards more sophisticated devices capable of detecting a wider range of cardiac arrhythmias. While the market exhibits significant growth potential, challenges remain. High initial costs associated with device acquisition and maintenance could limit market penetration, particularly in low- and middle-income countries. Data privacy concerns and regulatory hurdles surrounding the collection and transmission of sensitive patient data also pose obstacles. However, ongoing efforts to reduce device costs and strengthen data security regulations are expected to mitigate these challenges. The continued integration of advanced technologies like artificial intelligence and machine learning to enhance diagnostic accuracy and efficiency promises to further propel market growth in the coming years. A consistent CAGR of 4.1% suggests a steady, predictable trajectory, making the market attractive for both established players and emerging companies. The forecast period of 2025-2033 should see continued expansion, particularly as the technology becomes more readily available and affordable.