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

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

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Experimental photoplethysmography (PPG) dataset used for the assessment and validation of the mathematical model of PPG signals reported in A. Lefieux, J. Daraize, F. Vergnet, M. Vidrascu, M. Willemet, A. Bendjoudi, D. Lombardi, M.A. Fernández, "Mathematical modeling of photoplethysmography: model assessment and validation", hal-05134478, 2025. The PPG and pulse pressure measurements of this dataset were collected by Withings SAS during an internal experimental campaign involving 20 volunteers. The data was anonymized before being shared for research purposes, so that this dataset involves only secondary anonymized PPG and pulse pressure data. See the README file for a complete description of the data and files.

Correlations between pulse interval and R-R interval variability indices calculated from simultaneously recorded photoplethysmography (PPG) and electrocardiography (ECG) (n = 41).

Photoplethysmogram (PPG) signals collected from five subjects in three scenarios that vary in the level of activity, measured using Asiawill Pulse Heart Rate sensor implemented in an Arduino-based wearable sensor-kit.

The conventional approach to monitoring sleep stages requires placing multiple sensors on the patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single sensor Photoplethysmographic (PPG) based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of ten patients. Data analysis was performed to obtain 82 features from the recordings, which were then classified against the sleep stages. The classification results using SVM with the polynomial kernel gave the overall accuracy of 84.66%, 79.62%, and 72.23% for two, three, and four-stage sleep classification. These results show that using only PPG; it is possible to conduct sleep stage monitoring. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring., The PSG data were recorded for the night sleep duration of ten participants (9 male/ 1 female, age 43–75 years). The length of sleep time ranged from 6.8 to 10.1 hours. All participants were volunteers and recruited from the out-patients at Charite Hospital, Berlin, Germany. All suffered sleep-disordered breathing and were free from a history of cardiac issues. The diagnosis was based on PSG outcomes and clinical symptoms. The research and data collection protocol was approved by the Charite Hospital Committee for Ethics in Human Research (2018), Berlin, Germany, and the experiments were conducted in accordance with the Helsinki declaration for ethical experiments, revised in 2013. Written consent was taken prior to the experiments. The demographic information of the subjects. Each PSG recording included two-channel EEG (channel C3-A2 and C4-A1), ECG, PPG, left and right EOG, leg movements, thoracic and abdominal wall expansion, arterial oxygen saturation SaO2, and oronasal airflow. Acc..., Microsoft Excel or Matlab or Python.

This dataset contains the beat and artifact labels created for the comparison of beat detection algorithms for photoplethysmogram analysis as published in Karlen W, Ansermino JM, Dumont GA. "Adaptive Pulse Segmentation and Artifact Detection in Photoplethysmography for Mobile Applications". In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). San Diego; 2012. p. 3131–4. The labels are for the pox1 and pox2 recordings (125 Hz, children in a pediatric intensive care unit) of the Complex System Laboratory "CSL Pulse Oximetry (infrared) Beat Detection Benchmarks" available at http://bsp.pdx.edu/Data/ originally published in M. Aboy, J. McNames, T. Thong, D. Tsunami, M. S. Ellenby, and B. Goldstein, “An automatic beat detection algorithm for pressure signals.” IEEE Transactions on Biomedical Engineering, vol. 52, no. 10, pp. 1662–70, 2005.

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 sensors. This dataset contains 3096, 5 dimensional time series obtained from the IEEE Signal Processing Cup 2015: Heart Rate Monitoring During Physical Exercise Using Wrist-Type Photoplethysmographic (PPG) Signals. Two-channel PPG signals, three-axis acceleration signals, and one-channel ECG signals were simultaneously recorded from subjects with age from 18 to 35. For each subject, the PPG signals were recorded from wrist by two pulse oximeters with green LEDs (wavelength: 515nm). Their distance (from center to center) was 2 cm. The acceleration signal was also recorded from wrist by a three-axis accelerometer. Both the pulse oximeter and the accelerometer were embedded in a wristband, which was comfortably worn. The ECG signal was recorded simultaneously from the chest using wet ECG sensors. All signals were sampled at 125 Hz and sent to a nearby computer via Bluetooth.

Please refer to https://sites.google.com/site/researchbyzhang/ieeespcup2015 for more details.

Copyright
All datasets have copyrights. But you can freely use them for the Signal Processing Cup or your own academic research, as long as you suitably cite the data in your works.

Citation request
Z. Zhang, Z. Pi, B. Liu, TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise, IEEE Transactions on Biomedical Engineering, vol. 62, no. 2, pp. 522-531, February 2015, DOI: 10.1109/TBME.2014.2359372

The following data are provided from the PPG Diary 1 Study. Definition: PPG - photoplethysmogram

Versions:

• Preliminary Subsets v.0.1: Three subsets are provided: (i) a one-hour recording of simultaneous red and infrared PPG signals; (ii) a one-minute recording of infrared PPG signal; (iii) a recording of a single pulse wave, acquired using infrared PPG. Each recording was acquired from the same subject during sleep. The recordings are provided in Matlab ® format.

Further Information

Further information of the PPG Diary Project, and the data provided here, is available at: https://peterhcharlton.github.io/ppg-diary/

Correlations of PPG pulse interval and ECG R-R interval variability indices with sleep apnea indices (n = 41).

The Pulse Pattern Generator (PPG) market is experiencing robust growth, driven by increasing demand across diverse sectors. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors: the rising adoption of advanced technologies in semiconductor testing, the expansion of the aerospace and defense industries, and the increasing need for sophisticated signal testing equipment in telecommunications and research applications. The semiconductor segment is a significant revenue driver, with its intricate testing requirements necessitating high-performance PPGs. The dual-channel segment is expected to experience faster growth compared to single-channel solutions, owing to its capability to handle more complex test scenarios. Furthermore, technological advancements resulting in improved accuracy, speed, and functionality of PPGs are propelling market expansion.
However, certain restraints could potentially impact market growth. High initial investment costs for advanced PPG systems and the need for specialized expertise to operate these complex instruments could limit adoption in some sectors. Nevertheless, the continuous innovation in PPG technology, coupled with the increasing demand for enhanced testing capabilities, is expected to outweigh these challenges, ensuring a sustained growth trajectory for the PPG market throughout the forecast period. The competitive landscape is marked by several key players, including Keysight, Tektronix, and Tabor Electronics, who are investing in R&D to enhance their product offerings and cater to the evolving needs of various end-use industries. Geographic expansion, particularly in rapidly developing economies of Asia-Pacific, presents significant opportunities for market players.

Patients’ characteristics (n = 41).

IntroductionScientists and consumer products are increasingly employing light-based photoplethysmography (PPG) instead of electrocardiography (ECG) assuming it accurately quantifies heart rate variability (HRV). Recent studies, however, have demonstrated that pulse rate variability (PRV) derived from PPG is not equivalent to HRV-derived from ECG. This study investigated the agreement between PPG-PRV and ECG-HRV in a beat-to-beat analysis in 931 adults recruited from a tertiary academic medical center in the southeastern United States.MethodsParticipants wore two (chest and bicep) Warfighter Monitor™ devices (Tiger Tech Solutions, Inc.). Heart rate (HR), pulse rate (PR) and three time-domain indices for PPG-PRV and ECG-HRV were measured. ECG-derived RR and noise-filtered NN intervals were extracted to compute HR, SDNN (standard deviation of NN intervals), rMSSD (root mean square of successive differences), and pNN50 (percentage of successive NN intervals differing by >50 ms). PPG-derived pulse-wave peaks were detected to calculate corresponding PR/PRV metrics. Pearson correlation, Bland–Altman, and one-way ANOVA analyses assessed linear association, bias, and mean differences across select chronic diseases.ResultsSignificant disagreement and differences were observed between ECG-HRV and PPG-PRV (p < 0.001 for all). For rMSSD: cardiovascular: 3.04 ms, 95% CI: 1.33, 4.75, endocrine: 2.85 ms, 95% CI: 0.52, 5.18, and neurological: 4.39 ms, 95% CI: 1.39, 7.39). For SDNN: cardiovascular: 8.50 ms, 95% CI: 5.25, 11.74, endocrine: 8.43 ms, 95% CI: 3.97, 12.90, neurological: 11.84 ms, 95% CI: 6.02, 17.67, and respiratory: 7.23 ms, 95% CI: 1.83, 12.62). For pNN50: cardiovascular: 2.48%, 95% CI: 1.67, 3.3, endocrine: 2.21% 95% CI: 1.12, 3.29, neurological: 2.91%, 95% CI: 1.25, 4.32, and respiratory: 1.46%, 95% CI: 0.15, 2.77).DiscussionPPG-PRV is a poor surrogate for ECG- HRV as it significantly underestimated SDNN, rMSSD, and pNN50 across select chronic diseases. Given the widespread use of PPG-based devices and ubiquitous, incorrect assumption that PRV accurately reflects HRV, researchers, clinicians, and manufacturers must clearly distinguish between PRV and HRV in studies and product claims.

The CapnoBase TBME RR benchmark dataset contains 42 cases of 8-min recordings. In addition to the CO2 waveforms (capnograms), these cases have also the Photoplethysmogram (PPG) from pulse oximetry available. Labels from an expert are available for pulse peaks from PPG and breaths from CO2. Also, the benchmark contains the results from the Karlen et al. IEEE TBME paper on multi-parameter RR calculation. This can be used to directly compare algorithm performances. The benchmark dataset is used by researchers to test and compare algorithms. This dataset should not be used to train or tune an algorithm as it may bias the performance results.

The Pulse Pattern Generator (PPG) market is experiencing robust growth, driven by increasing demand across various sectors. While precise market size figures for the base year (2025) are unavailable, a logical estimation based on industry trends and the presence of major players like Keysight, Tektronix, and Tabor Electronics suggests a market value of approximately $500 million in 2025. Considering the substantial investments in research and development within the electronics and telecommunications industries, coupled with the rising adoption of advanced technologies like 5G and high-speed data communication, the market is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth trajectory is fueled by several key drivers, including the need for precise and reliable testing equipment in the design and manufacturing of advanced electronics, the expanding semiconductor industry, and the increasing complexity of modern communication systems. The development of highly integrated PPGs with improved performance characteristics and user-friendly interfaces also contributes significantly to market expansion. The PPG market segmentation is broad, encompassing diverse applications across various industries. Key segments likely include high-speed communication testing, aerospace and defense, automotive testing, and research & development. While specific regional data is not provided, North America and Europe are expected to hold substantial market shares, driven by a strong presence of established companies and a high concentration of advanced technology applications. Restraints to market growth may include the high cost of advanced PPGs and the emergence of alternative testing methodologies. However, the continuous advancement in technology and the increasing demand for high-performance testing solutions are anticipated to offset these constraints, ensuring continued market expansion in the coming years.

Photoplethysmography (PPG) is a useful tool for monitoring pulse and oxygenation. Pulse transit time (PTT) calculated as the time interval between heart beat and peripheral pulse provides additional information about the cardiovascular system. However, not all off-the-shelf PPG and electrocardiogram (ECG) monitoring devices, even integrated together, are suitable for this purpose. In this retrospective clinical study of PTT signal collected in 35 subjects. We present a problem with the PTT signal referred to as sawtooth artifact. We also propose an approach based on the de-shape method to visualize and quantify the sawtooth artifact to confirm it as an artifact. One file represents one kind of time-series data. Filenames contain "Wave500Hz_ECG_II", "Wave500Hz_Pleth", "Trend4Hz_PPG_PTT", and "Trend4Hz_RRI_DN" represent the lead II electrocardiography raw data in 500Hz sampling rate, the photo-plethysmography waveform data in 500Hz sampling rate, the derived pulse transit time data in 4Hz sampling rate, and the derived R-R peak interval in 4Hz sampling rate respectively. Data from each case contain these 4 files so that the viewer can check the sawtooth artifact directly from the "*Trend4Hz_PPG_PTT.txt" data, or calculate the PTT data from the ECG signal and the PPG signal by himself. The PDF files are the result of time-frequency analysis showing the constant frequency of the sawtooth artifact. The "*.m" files are the MATLAB routines the authors used to perform the time-frequency analysis by the "de-shape" method.

IntroductionScientists and consumer products are increasingly employing light-based photoplethysmography (PPG) instead of electrocardiography (ECG) assuming it accurately quantifies heart rate variability (HRV). Recent studies, however, have demonstrated that pulse rate variability (PRV) derived from PPG is not equivalent to HRV-derived from ECG. This study investigated the agreement between PPG-PRV and ECG-HRV in a beat-to-beat analysis in 931 adults recruited from a tertiary academic medical center in the southeastern United States.MethodsParticipants wore two (chest and bicep) Warfighter Monitor™ devices (Tiger Tech Solutions, Inc.). Heart rate (HR), pulse rate (PR) and three time-domain indices for PPG-PRV and ECG-HRV were measured. ECG-derived RR and noise-filtered NN intervals were extracted to compute HR, SDNN (standard deviation of NN intervals), rMSSD (root mean square of successive differences), and pNN50 (percentage of successive NN intervals differing by >50 ms). PPG-derived pulse-wave peaks were detected to calculate corresponding PR/PRV metrics. Pearson correlation, Bland–Altman, and one-way ANOVA analyses assessed linear association, bias, and mean differences across select chronic diseases.ResultsSignificant disagreement and differences were observed between ECG-HRV and PPG-PRV (p < 0.001 for all). For rMSSD: cardiovascular: 3.04 ms, 95% CI: 1.33, 4.75, endocrine: 2.85 ms, 95% CI: 0.52, 5.18, and neurological: 4.39 ms, 95% CI: 1.39, 7.39). For SDNN: cardiovascular: 8.50 ms, 95% CI: 5.25, 11.74, endocrine: 8.43 ms, 95% CI: 3.97, 12.90, neurological: 11.84 ms, 95% CI: 6.02, 17.67, and respiratory: 7.23 ms, 95% CI: 1.83, 12.62). For pNN50: cardiovascular: 2.48%, 95% CI: 1.67, 3.3, endocrine: 2.21% 95% CI: 1.12, 3.29, neurological: 2.91%, 95% CI: 1.25, 4.32, and respiratory: 1.46%, 95% CI: 0.15, 2.77).DiscussionPPG-PRV is a poor surrogate for ECG- HRV as it significantly underestimated SDNN, rMSSD, and pNN50 across select chronic diseases. Given the widespread use of PPG-based devices and ubiquitous, incorrect assumption that PRV accurately reflects HRV, researchers, clinicians, and manufacturers must clearly distinguish between PRV and HRV in studies and product claims.

The Pulse Wave Database The Pulse Wave Database (PWDB) is a database of simulated arterial pulse waves designed to be representative of a sample of pulse waves measured from healthy adults. It contains pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database contains a baseline set of pulse waves for each of the six age groups, created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It also contains 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. The entire database is available at DOI: 10.5281/zenodo.2633174 . This dataset: baseline subjects aged 25 to 75 This dataset is a subset of the PWDB. It contains the pulse waves for the six baseline subjects aged 25 to 75 (in 10 year increments). It contains the following waves: arterial flow velocity (U), luminal area (A), pressure (P), and photoplethysmogram (PPG). These pulse waves are provided at a range of measurement sites, including: aorta (ascending and descending) carotid artery brachial artery radial artery finger femoral artery The data are available in three formats: Matlab, CSV and WaveForm Database (WFDB) format. Further details of the formatting and contents of each file are available at: https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database Accompanying Publication This is a subset of the PWDB database, which is described in the following publication: Charlton P.H., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P. & Alastruey, J., “Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices", AJP Hear. Circ. Physiol. [in press], 2019. Please cite this publication when using the database. Further Information Further information on the Pulse Wave Database project can be found at: https://peterhcharlton.github.io/pwdb/ Version History Version 1.0 : provided for peer review of "Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices" This work was supported by the British Heart Foundation [PG/15/104/31913], the Wellcome EPSRC Centre for Medical Engineering at King's College London [WT 203148/Z/16/Z], and the King's College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging [EP/L015226/1]. The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative at Guy's and St Thomas' NHS Foundation Trust (GSTT). The views expressed are those of the authors and not necessarily those of the BHF, Wellcome Trust, EPSRC, NIHR or GSTT.

The Photoplethysmography (PPG) biosensor market is experiencing robust growth, driven by the increasing demand for wearable health monitoring devices and the rising prevalence of chronic diseases. The market, estimated at $1.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $5 billion by 2033. This growth is fueled by several key factors. Firstly, advancements in miniaturization and power efficiency of PPG sensors are enabling their integration into a wider range of wearable devices, including smartwatches, fitness trackers, and even clothing. Secondly, the rising adoption of remote patient monitoring (RPM) programs and telehealth services is increasing the demand for cost-effective and accurate PPG-based biosensors for continuous health data acquisition. Furthermore, the increasing awareness of personal health and wellness among consumers is driving the adoption of consumer-grade wearable health trackers, further boosting market growth. Segmentation analysis reveals that heart rate monitoring currently holds the largest application share, followed by blood-oxygen saturation monitoring. Pulse oximeters remain the dominant product type, though smartwatches and wristbands are rapidly gaining market share due to their user-friendly interface and multi-functionality. Geographic analysis indicates that North America and Europe currently dominate the market, however, the Asia-Pacific region is projected to witness significant growth due to rising disposable incomes and expanding healthcare infrastructure. Despite the positive outlook, the market faces challenges. High initial costs of advanced PPG sensors and concerns regarding data privacy and security could hinder market expansion. Additionally, the accuracy of PPG measurements can be affected by factors like motion artifacts and skin pigmentation, necessitating further technological advancements to improve reliability. However, ongoing research and development efforts focused on improving sensor accuracy, integrating advanced algorithms, and addressing privacy concerns are expected to mitigate these challenges and fuel continued market expansion in the coming years. The competitive landscape is characterized by a mix of established players such as Maxim Integrated and Murata Manufacturing and emerging innovative companies like Valencell, each vying for market dominance through product innovation and strategic partnerships.

The global pulse oximetry sensors market is experiencing steady growth, driven by increasing prevalence of chronic diseases requiring continuous monitoring, rising geriatric population, and technological advancements leading to smaller, more accurate, and wireless sensors. The market's Compound Annual Growth Rate (CAGR) of 5% from 2019 to 2024 suggests a substantial market expansion, and a projected continuation of this growth trajectory through 2033. Key players like Masimo Corporation, Johnson & Johnson, Medtronic, and Nonin are actively contributing to this growth through product innovation and strategic partnerships. Market segmentation is likely driven by sensor type (e.g., reusable vs. disposable), application (e.g., hospital, homecare), and technology (e.g., photoplethysmography (PPG), spectroscopic). While the precise market size for 2025 is unavailable, extrapolating from a reasonable assumption of a $5 billion market size in 2024 and applying the 5% CAGR, the 2025 market size is estimated at approximately $5.25 billion. Factors restraining market growth include the relatively high cost of advanced sensors, concerns surrounding sensor accuracy and potential for false readings, and regulatory hurdles in certain regions for new device approvals. However, ongoing technological innovation focused on improving sensor accuracy, miniaturization, and integration with wearable health monitoring devices is likely to mitigate these restraints. The increasing integration of pulse oximetry sensors into wearable technology, remote patient monitoring systems, and point-of-care diagnostics further fuels market expansion, particularly in home healthcare settings. The North American and European markets currently dominate the pulse oximetry sensor landscape but developing economies in Asia-Pacific are expected to witness significant growth in the coming years due to rising healthcare expenditure and improved healthcare infrastructure.