there may be missing datum.

Data file contains heart rate values for each fish by experiment. The data were used to generate figures and tables in the attached manuscript.

This dataset is associated with the following publication: Martin, W.K., A. Tennant, R. Conolly, K. Prince, J. Stevens, D. DeMarini, B. Martin, L. Thompson, I. Gilmour, W. Cascio, M. Hays, M. Hazari, S. Padilla, and A. Farraj. High-Throughput Video Processing to Score Heart Rate Responses to Xenobiotics in Wild-type Embryonic Zebrafish per imaging field. Scientific Reports. Nature Publishing Group, London, UK, 9(1): 145, (2019).

VIPL-HR database is a database for remote heart rate (HR) estimation from face videos under less-constrained situations. It contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Nine different conditions, including various head movements and illumination conditions are taken into consideration. All the videos are recorded using Logitech C310, RealSense F200 and the front camera of HUAWEI P9 smartphone, and the ground-truth HR is recorded using a CONTEC CMS60C BVP sensor (a FDA approved device).

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

where the finger outline was extracted from the video.

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to estimate heart rate using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.

Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details

Relevant papers Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. DOI: 10.1109/TBME.2016.2613124.

Citation request Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. DOI: 10.1109/TBME.2016.2613124. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

HRV data of a cohort of pro-cyclists. All the HRV variables are available here. Any use of this dataset without permission is prohibited.

Each stable of the Korea Racing Authority provides collected data such as ECG and heart rate of horses during horse training. (The provided information is horse number (hrNo), horse name (hrNm), equipment sequence number (deviceSeq), measurement date (mesurDy), measurement date and time (mesurDt), ECG (ecg), and heart rate (hr) data.) - You can search the data using the measurement date (mesur_dy), horse number (hr_no), page number (pageNo), and the number to be displayed per page (numOfRows) in the request message. - If there is no request message, the data of the most recent measurement date among the data is displayed.

In a previous publication the database of our phase 0 study on the feasibility of non-contact vital signs monitoring was published. Based on the data it has been shown that besides pulse and respiration, radar systems are also capable to measure the heart sounds of the test persons. The heart sound offers a significant advantage when trying to measure the heartbeat as precisely as possible, as it has a very concise and sharp morphology. This precision is, e.g., very important for determining the parameters of heart rate variability.Following a successful phase 0, a clinical evaluation of the radar has been planned and performed. In phase 1, 30 healthy subjects were measured with the radar and a synchronised reference device, the so-called Task Force Monitor. In addition to non-contact continuous blood pressure, the Task Force Monitor can measure ECG, ICG and impedance. It also evaluates the raw signals and determines additional values such as heart rate, stroke volume, cardiac output, thoracic fluid content, total peripheral resistance, and many more.Besides a resting scenario, scenarios such as the Valsalva maneuver, tilt table test or breath holding were performed to evoke different triggers of the autonomic nervous system. The measurements were performed and monitored by medical professionals at the university hospital Erlangen. The study was approved by the ethics committee of the Friedrich-Alexander-Universität Erlangen-Nürnberg (No. 85_15B). All research was performed in accordance with relevant guidelines and regulations. The informed consent was obtained from all subjects in human trials.Due to size constrains the dataset is split in three zip files. Download the files and unzip them in one folder using a tool like "7zip" to get the whole database.Important:Information on how to use the database and the measurements can be found in the corresponding publication: https://www.nature.com/articles/s41597-020-00629-5

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Heart Rate Variability Analysis System Market size was valued at USD 13.53 Billion in 2023 and is projected to reach USD 37.23 Billion by 2031, growing at a CAGR of 10.7% during the forecast period 2024-2031.

Global Heart Rate Variability Analysis System Market Drivers

The market drivers for the Heart Rate Variability Analysis System Market can be influenced by various factors. These may include:

Data security and privacy issues: Since the healthcare industry handles sensitive patient data, outsourcing RCM procedures may give rise to questions with data security, privacy, and third-party vendor trust, as well as regulatory compliance (such as HIPAA in the US).

Issues with Regulatory Compliance: There are many regulations in the healthcare sector. Modifications to healthcare laws, billing standards, or policies may make it difficult for outsourcing partners to maintain compliance, which could result in inefficient operations.

Global Heart Rate Variability Analysis System Market Restraints

Several factors can act as restraints or challenges for the Heart Rate Variability Analysis System Market. These may include:

Data security and privacy issues: Since the healthcare industry handles sensitive patient data, outsourcing RCM procedures may give rise to questions with data security, privacy, and third-party vendor trust, as well as regulatory compliance (such as HIPAA in the US).

Issues with Regulatory Compliance: There are many regulations in the healthcare sector. Modifications to healthcare laws, billing standards, or policies may make it difficult for outsourcing partners to maintain compliance, which could result in inefficient operations.

Data on county socioeconomic status for 2,132 US counties and each county’s average annual cardiovascular mortality rate (CMR) and total PM2.5 concentration for 21 years (1990-2010). County CMR, PM2.5, and socioeconomic data were obtained from the U.S. National Center for Health Statistics, U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system, and the U.S. Census, respectively. A socioeconomic index was created using seven county-level measures from the 1990 US census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This dataset is associated with the following publication: Wyatt, L., G. Peterson, T. Wade, L. Neas, and A. Rappold. The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 136: 105430, (2020).

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.

This dataset is for the article entitled: “Validity of Heart Rate Measurements in Wrist-Based Monitors Across Skin Tones During Exercise”.

This dataset contains the data that was a basis for the results discussed in the paper “Persistent homology as a new method of the assessment of heart rate variability” by Grzegorz Graff, Beata Graff, Paweł Pilarczyk, Grzegorz Jabłoński, Dariusz Gąsecki, Krzysztof Narkiewicz, Plos One (2021), DOI: 10.1371/journal.pone.0253851.

PPG-DaLiA is a publicly available dataset for PPG-based heart rate estimation. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects while performing a wide range of activities under close to real-life conditions. The included ECG data provides heart rate ground truth. The included PPG- and 3D-accelerometer data can be used for heart rate estimation, while compensating for motion artefacts.

Data heart rate dan SpO2 dari subjek penelitian.

Brno University of Technology Smartphone PPG Database (BUT PPG) is a database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating PPG quality and estimation of heart rate (HR). The data comprises 3,888 10-second recordings of PPGs and associated ECG signals used for determination of reference HR. All of the signals contain QRS complex positions, most signals contain moreover accelerometric data (ACC), and single-value annotations of blood pressure, blood oxygen saturation and glycaemia. The data were collected from 50 subjects (25 female, 25 male) aged between 19 to 76 years at rest and during various types of movement. Recordings were carried out between August 2020 and December 2021. PPG data were collected by smartphones Xiaomi Mi9 and Huawei P20 Pro with sampling frequency of 30 Hz. Reference ECG signals and ACC data were recorded using a mobile recorder Bittium Faros 360 or 180 with a sampling frequency of 1,000 Hz (ECG) and 100 Hz (ACC). Each PPG signal includes annotation of quality and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably and thus these signals are unsuitable for any analysis.

The heart attack datasets were collected at Zheen hospital in Erbil, Iraq, from January 2019 to May 2019. The attributes of this dataset are: age, gender, heart rate, systolic blood pressure, diastolic blood pressure, blood sugar, ck-mb and troponin with negative or positive output. According to the provided information, the medical dataset classifies either heart attack or none. The gender column in the data is normalized: the male is set to 1 and the female to 0. The glucose column is set to 1 if it is > 120; otherwise, 0. As for the output, positive is set to 1 and negative to 0.

Overview The Human Vital Signs Dataset is a comprehensive collection of key physiological parameters recorded from patients. This dataset is designed to support research in medical diagnostics, patient monitoring, and predictive analytics. It includes both original attributes and derived features to provide a holistic view of patient health.

Attributes Patient ID

Description: A unique identifier assigned to each patient. Type: Integer Example: 1, 2, 3, ... Heart Rate

Description: The number of heartbeats per minute. Type: Integer Range: 60-100 bpm (for this dataset) Example: 72, 85, 90 Respiratory Rate

Description: The number of breaths taken per minute. Type: Integer Range: 12-20 breaths per minute (for this dataset) Example: 16, 18, 15 Timestamp

Description: The exact time at which the vital signs were recorded. Type: Datetime Format: YYYY-MM-DD HH:MM Example: 2023-07-19 10:15:30 Body Temperature

Description: The body temperature measured in degrees Celsius. Type: Float Range: 36.0-37.5°C (for this dataset) Example: 36.7, 37.0, 36.5 Oxygen Saturation

Description: The percentage of oxygen-bound hemoglobin in the blood. Type: Float Range: 95-100% (for this dataset) Example: 98.5, 97.2, 99.1 Systolic Blood Pressure

Description: The pressure in the arteries when the heart beats (systolic pressure). Type: Integer Range: 110-140 mmHg (for this dataset) Example: 120, 130, 115 Diastolic Blood Pressure

Description: The pressure in the arteries when the heart rests between beats (diastolic pressure). Type: Integer Range: 70-90 mmHg (for this dataset) Example: 80, 75, 85 Age

Description: The age of the patient. Type: Integer Range: 18-90 years (for this dataset) Example: 25, 45, 60 Gender

Description: The gender of the patient. Type: Categorical Categories: Male, Female Example: Male, Female Weight (kg)

Description: The weight of the patient in kilograms. Type: Float Range: 50-100 kg (for this dataset) Example: 70.5, 80.3, 65.2 Height (m)

Description: The height of the patient in meters. Type: Float Range: 1.5-2.0 m (for this dataset) Example: 1.75, 1.68, 1.82 Derived Features Derived_HRV (Heart Rate Variability)

Description: A measure of the variation in time between heartbeats. Type: Float Formula: 𝐻 𝑅

𝑉

Standard Deviation of Heart Rate over a Period Mean Heart Rate over the Same Period HRV= Mean Heart Rate over the Same Period Standard Deviation of Heart Rate over a Period ​

Example: 0.10, 0.12, 0.08 Derived_Pulse_Pressure (Pulse Pressure)

Description: The difference between systolic and diastolic blood pressure. Type: Integer Formula: 𝑃

𝑃

Systolic Blood Pressure − Diastolic Blood Pressure PP=Systolic Blood Pressure−Diastolic Blood Pressure Example: 40, 45, 30 Derived_BMI (Body Mass Index)

Description: A measure of body fat based on weight and height. Type: Float Formula: 𝐵 𝑀

𝐼

Weight (kg) ( Height (m) ) 2 BMI= (Height (m)) 2

Weight (kg) ​

Example: 22.8, 25.4, 20.3 Derived_MAP (Mean Arterial Pressure)

Description: An average blood pressure in an individual during a single cardiac cycle. Type: Float Formula: 𝑀 𝐴

𝑃

Diastolic Blood Pressure + 1 3 ( Systolic Blood Pressure − Diastolic Blood Pressure ) MAP=Diastolic Blood Pressure+ 3 1 ​ (Systolic Blood Pressure−Diastolic Blood Pressure) Example: 93.3, 100.0, 88.7 Target Feature Risk Category Description: Classification of patients into "High Risk" or "Low Risk" based on their vital signs. Type: Categorical Categories: High Risk, Low Risk Criteria: High Risk: Any of the following conditions Heart Rate: > 90 bpm or < 60 bpm Respiratory Rate: > 20 breaths per minute or < 12 breaths per minute Body Temperature: > 37.5°C or < 36.0°C Oxygen Saturation: < 95% Systolic Blood Pressure: > 140 mmHg or < 110 mmHg Diastolic Blood Pressure: > 90 mmHg or < 70 mmHg BMI: > 30 or < 18.5 Low Risk: None of the above conditions Example: High Risk, Low Risk This dataset, with a total of 200,000 samples, provides a robust foundation for various machine learning and statistical analysis tasks aimed at understanding and predicting patient health outcomes based on vital signs. The inclusion of both original attributes and derived features enhances the richness and utility of the dataset.