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.

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.

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Cuff-less Blood Pressure Measurement based on Four-wavelength PPG SignalsThis dataset was collected primarily to explore the role of PPG signals with different wavelengths in the prediction of cuffless blood pressure. The PPG signals are collected at the human index finger which are the reflex type. This dataset can be used to study data mining of PPG signals with different wavelengths, or it can be used to build novel cuffless blood pressure measurement models using single or multiple PPG signals.The dataset contains four-wavelength PPG signals and blood pressure values measured by OMRON HEM-7201. There are data files "ppg_data" and physiological information files in the dataset. The physiological information files are saved as an Excel document named as "Subject Information.xlsx". SBP and DBP represent systolic blood pressure and diastolic blood pressure. SBP and DBP were measured before PPG signal collection and PPG signal collection begins immediately after blood pressure measurement.The dataset contains a total of 180 subjects, each with 60 seconds signal length and 200 Hz sampling frequency. The wavelength information of signals in the dataset is listed as follows：channel1: 660nmchannel2: 730nmchannel3: 850nmchannel4: 940nmThe above dataset is collected and managed by CardioWorks Team. If you have any questions about the data or relative researches, please contact us by email: liangyongbo@guet.edu.cn or liangyongbo001@gmail.com.The CardioWorks Team focuses on PPG-based portable or wearable cardiovascular health detection and disease assessment. For more research datasets and published papers, please pay attention to the following:Dataset:PPG-BP Database: https://doi.org/10.6084/m9.figshare.5459299.v5Non-invasive Hemoglobin Detection based on Four-wavelength PPG Signal: https://doi.org/10.6084/m9.figshare.22256143.v1Cuff-less Blood Pressure Measurement based on Four-wavelength PPG Signals：https://doi.org/10.6084/m9.figshare.23283518.v1Published Articles:[1] Mohamed Elgendi, Richard Fletcher, Yongbo Liang, et al. The use of photoplethysmography for assessing hypertension [J]. npj Digital Medicine, 2019, 2(1):1-11.（2019）Link[2] Xudong Hu Shimin Yin, Xizhuang Zhang, Carlo Menon, Cheng Fang, Zhencheng Chen, Mohamed Elgendi* and Yongbo Liang*. Blood pressure stratification using photoplethysmography and light gradient boosting machine [J]. Frontiers in Physiology, 2023, 14(1072273): 1-11.（2023）Link[3] Yongbo Liang, Shimin Yin, Qunfeng Tang, Zhenyu Zheng, Mohamed Elgendi* and Zhencheng Chen*. Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals. Frontiers in Physiology, 02 October 2020. Doi: 10.3389/fphys.2020.569050. （2020）Link[4] Cheng, Peng，Chen, Zhencheng*，Li, Quanzhong，Gong, Qiong，Zhu, Jianming，Liang, Yongbo*. Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning. IEEE Access 8, 172692-172706 (2020). Link[5] Zhenyu Zheng, Zhencheng Chen*, Fangrong Hu, Jianming Zhu, Qunfeng Tang, Yongbo Liang*. An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology [J]. Electronics, 2020, 9(1): 1-15. Link[6] Yongbo Liang, Derek Abbott, Newton Howard, Kenneth Lim, Rabab Ward and Mohamed Elgendi*. How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database. Journal of Clinical Medicine, 8， 1-14, doi:10.3390/jcm8030337 (2019). Link[7] Yongbo Liang, Zhencheng Chen*, Guiyong Liu, Mohamed Elgendi*. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Scientific data, doi:10.1038/sdata.2018.20 (2018). Link[8] Yongbo Liang, Mohamed Elgendi*, Zhencheng Chen* & Rabab Ward. An optimal filter for short photoplethysmogram signals. Scientific data, 5, 180076, doi:10.1038/sdata.2018.76 (2018). Link[9] Yongbo Liang, Zhencheng Chen*, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach. Journal of Clinical Medicine, 8, doi:10.3390/jcm8010012 (2018). Link[10] Yongbo Liang, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics, 8, doi:10.3390/diagnostics8030065 (2018). Link[11] Yongbo Liang, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors, 8，doi:10.3390/bios8040101 (2018). Link[12] Xuhao Dong Ziyi Wang, Liangli Cao, Zhencheng Chen*, Yongbo Liang*. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals [J]. Diagnostics, 2023, 13(5): 1-14. Link[13] Zhencheng Chen, Huishan Qin, Wenjun Ge, Shiyong Li*, Yongbo Liang*. Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography [J]. Electronics, 2023, 12(6): 1-12. Link[14] Yang Zhang, Jianming Zhu, Yongbo Liang, Hongbo Chen, Shimin Yin and Zhencheng Chen*. Non-invasive blood glucose detection system based on conservation of energy method. Physiological measurement, 2017, 38: 325-342.[15] Yongbo Liang, Ahmed Hussain, Derek Abbott, Carlo Menon, Rabab Ward and Mohamed Elgendi*. Impact of Data Transformation: An ECG Heartbeat Classification Approach. Frontiers in Digital Health, Dec 23, 2020 doi: 10.3389/fdgth.2020.610956 (2020), Link

Overview This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session. Database The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: https://pubmed.ncbi.nlm.nih.gov/30094756/ In addition, PPG signals corrupted by the noise generator at different levels are also included in the database. Realistic noise generator Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper. Data Files The reference PPG signal can be found in Datasets\GoodSignals\PPG and the simultaneously acquired ECG in Datasets\GoodSignals\ECG. The folder Datasets\NoisySignals contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation). Name convention: ppg_stdx_y denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x. Datasets\BPMs contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s. Code The folder Code contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator. When referencing this material, please cite: Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. Sensors 2021, 21, 2725. https://doi.org/10.3390/s21082725

PPGBP dataset: The short-recorded PPG dataset collected and shared by Liang et al. [4]. It contains 219 subjects, with one BP reading followed by three PPG segments with a duration of 2.1 seconds each. Thus, it is the smallest set in the number of segments (613) but with a relatively high number of subjects. The original sampling frequency of 1000 Hz was resampled at 125 Hz. We conducted a 5-fold CV to partition the data once it was preprocessed.

[4] Liang, Y., Chen, Z., Liu, G. & Elgendi, M. PPG-BP database (2018). Figshare https://doi.org/10.6084/m9.figshare.5459299.

Non-invasive monitoring and surveillance methods of blood glucose measurement can provide ease of use and simplicity for different individuals while reducing the risks and damages of invasive methods. The non-invasive method based on photoplethysmography (PPG) signal is one of the innovative methods on this topic which numerous studies have been conducted by research centers and various companies. However, due to various reasons, the reviewed dataset was not available and no standard dataset has been published on this topic. The presented dataset, which was sampled by the research team of the digital systems of the University of Science and Technology in Mazandaran, Behshahr, Iran, includes 67 raw PPG signals with 2175 Hz sampling frequency along with labeled data: age, gender, and the invasive blood glucose level which can be used in other studies and learning algorithms.

Derivative of the MIMIC IV Waveform Database formatted to be suitable for machine learning. Formatting All records are split into intervals of roughly 60 seconds. The parameter values are averaged over each 60 second interval. The PPG signal data are unprocessed, i.e. as in the original dataset. Intervals with PPG signals containing missing data or large constant data are excluded. PPG signals and signal times are truncated to have the same amount of data points for all records. Formatted data are split into 3 different file types, namely *_n.csv containing the averaged parameter values, *_s.npy containing PPG signal data and t.npy containing the respective signal measurement times. Moreover, formatted data are split into trainXX, validation_* and test_* data files, where the training data trainXX_* are split into multiple files for easier handling. This dataset was created using the following code: https://gitlab.com/qumphy/wp1-benchmark-data-conversion Funding The creation of this dataset has been supported by the European Partnership on Metrology programme 22HLT01 QUMPHY. This project (22HTL01 QUMPHY) has received funding from the EMPIR programme cofinanced by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.

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.

Sure, here is the information translated into English:This repository contains a dataset that includes 13 patient records from the MIMIC database. Each patient is represented by a .mat file that stores PPG (Photoplethysmography) and ECG (Electrocardiogram) signals with a sampling frequency of 125 Hz.Additionally, the repository includes MATLAB scripts for signal visualization and the detection of Type 1 and Type 2 Premature Ventricular Complexes (PVCs). These scripts allow for detailed analysis of the PPG and ECG signals for each patient.Dataset StructureThe repository is composed of the following files and directories:XXXm.mat Files: These files contain the PPG and ECG signals of the patients. Each file in the format m.mat corresponds to a specific patient and contains signal samples obtained in a clinical setting.señales_seleccionadasXXX.mat Files: These files specifically contain the PPG and ECG channel signals from the m.mat files of each patient.MATLAB Code (senales_ppg_ecg_visualizacion.m): A MATLAB script designed to visualize the ECG and PPG signals of each patient. This code allows loading the .mat files and displaying a segment of the signals to facilitate visual analysis. It is important to modify this line of code to select the patient to be analyzed:data = load('señales_seleccionadas212.mat'); % Load the .mat fileMATLAB Code (cpvs12.m): Another MATLAB script that implements the detection of Type 1 and Type 2 PVCs in the PPG signal. The results of this detection are stored in a CSV file, which can be consulted later for detailed signal analysis.Header Files (XXXhea.txt): These files contain the specifications for selecting the signals of each patient, allowing an understanding of the structure and available channels in the .mat files.Signal DescriptionsPPG (Photoplethysmography): Signals obtained using an optical sensor that measures changes in blood volume in tissues. These signals are useful for studying the pulse and other hemodynamic characteristics of patients.ECG (Electrocardiogram): Electrical signals of the heart used to evaluate cardiac activity. The files in this dataset include several ECG leads that provide detailed information about heart function.I hope this translation helps. If you need further modifications or have any other requests, feel free to let me know!

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.

Non-invasive Hemoglobin Detection based on Four-wavelength PPG SignalThis dataset was collected primarily to explore the role of PPG signals with different wavelengths in the prediction of non-invasive hemoglobin measurement. The PPG signals are collected at the human index finger which are the reflex type. This dataset can be used to study data mining of PPG signals with different wavelengths, or it can be used to build novel non-invasive hemoglobin measurement models using single or multiple PPG signals.The dataset contains a total of 152 subjects, each with signal length in the range of 30 seconds to one minute, and 200 Hz sampling frequency.The data of subject number 1-58 correspond to subjects numbered 1-58 in version 1 (posted on march, 2023), Data of subject number 59-78 correspond to the extra data numbered 1-20 in version 3 (posted on september, 2023). Others are supplementary data.The wavelength information of signals in the dataset is listed as follows:channel 1: 660 nmchannel 2: 730 nmchannel 3: 850 nmchannel 4: 940 nm------------------------------------------------------------------------------------------------------------------------------The above dataset is collected and managed by CardioWorks Team. If you have any questions about the data or relative researches, please contact us by email: liangyongbo@guet.edu.cn or liangyongbo001@gmail.com.The CardioWorks Team focuses on PPG-based portable or wearable cardiovascular health detection and disease assessment. For more research datasets and published papers, please pay attention to the following:Dataset:PPG-BP Database: https://doi.org/10.6084/m9.figshare.5459299.v5Non-invasive Hemoglobin Detection based on Four-wavelength PPG Signal: https://doi.org/10.6084/m9.figshare.22256143.v1Cuff-less Blood Pressure Measurement based on Four-wavelength PPG Signals：https://doi.org/10.6084/m9.figshare.23283518.v1Published Articles:[1] Mohamed Elgendi, Richard Fletcher, Yongbo Liang, et al. The use of photoplethysmography for assessing hypertension [J]. npj Digital Medicine, 2019, 2(1):1-11.（2019）Link[2] Xudong Hu Shimin Yin, Xizhuang Zhang, Carlo Menon, Cheng Fang, Zhencheng Chen, Mohamed Elgendi* and Yongbo Liang*. Blood pressure stratification using photoplethysmography and light gradient boosting machine [J]. Frontiers in Physiology, 2023, 14(1072273): 1-11.（2023）Link[3] Yongbo Liang, Shimin Yin, Qunfeng Tang, Zhenyu Zheng, Mohamed Elgendi* and Zhencheng Chen*. Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals. Frontiers in Physiology, 02 October 2020. Doi: 10.3389/fphys.2020.569050. （2020）Link[4] Cheng, Peng，Chen, Zhencheng*，Li, Quanzhong，Gong, Qiong，Zhu, Jianming，Liang, Yongbo*. Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning. IEEE Access 8, 172692-172706 (2020). Link[5] Zhenyu Zheng, Zhencheng Chen*, Fangrong Hu, Jianming Zhu, Qunfeng Tang, Yongbo Liang*. An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology [J]. Electronics, 2020, 9(1): 1-15. Link[6] Yongbo Liang, Derek Abbott, Newton Howard, Kenneth Lim, Rabab Ward and Mohamed Elgendi*. How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database. Journal of Clinical Medicine, 8， 1-14, doi:10.3390/jcm8030337 (2019). Link[7] Yongbo Liang, Zhencheng Chen*, Guiyong Liu, Mohamed Elgendi*. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Scientific data, doi:10.1038/sdata.2018.20 (2018). Link[8] Yongbo Liang, Mohamed Elgendi*, Zhencheng Chen* & Rabab Ward. An optimal filter for short photoplethysmogram signals. Scientific data, 5, 180076, doi:10.1038/sdata.2018.76 (2018). Link[9] Yongbo Liang, Zhencheng Chen*, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach. Journal of Clinical Medicine, 8, doi:10.3390/jcm8010012 (2018). Link[10] Yongbo Liang, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics, 8, doi:10.3390/diagnostics8030065 (2018). Link[11] Yongbo Liang, Zhencheng Chen, Rabab Ward & Mohamed Elgendi*. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors, 8，doi:10.3390/bios8040101 (2018). Link[12] Xuhao Dong Ziyi Wang, Liangli Cao, Zhencheng Chen*, Yongbo Liang*. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals [J]. Diagnostics, 2023, 13(5): 1-14. Link[13] Zhencheng Chen, Huishan Qin, Wenjun Ge, Shiyong Li*, Yongbo Liang*. Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography [J]. Electronics, 2023, 12(6): 1-12. Link[14] Yang Zhang, Jianming Zhu, Yongbo Liang, Hongbo Chen, Shimin Yin and Zhencheng Chen*. Non-invasive blood glucose detection system based on conservation of energy method. Physiological measurement, 2017, 38: 325-342.[15] Yongbo Liang, Ahmed Hussain, Derek Abbott, Carlo Menon, Rabab Ward and Mohamed Elgendi*. Impact of Data Transformation: An ECG Heartbeat Classification Approach. Frontiers in Digital Health, Dec 23, 2020 doi: 10.3389/fdgth.2020.610956 (2020), Link

Over the course of 24 hours, we collected raw (Photoplethysmography (PPG), Acceleration, and Gyro) and processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data samples. Biostrap approaches health insights from a data-driven perspective. Our clinical-grade hardware enables users to accurately track SpO2, HRV, RHR, and a variety of other biometrics with confidence.

This dataset consists of raw 64-channel EEG, cardiovascular (electrocardiography and photoplethysmography), and pupillometry data from 86 human participants during 4 minutes of eyes-closed resting and during performance of a classic working memory task – digit span task with serial recall. The participants either memorized (memory) or just listened to (control condition) sequences of 5, 9, or 13 digits presented auditorily with 2 second stimulus onset asynchrony. The dataset can be used for (1) developing algorithms for cognitive load discrimination and detection of cognitive overload; (2) studying neural (event-related potentials and brain oscillations) and peripheral physiological (electrocardiography, photoplethysmography, and pupillometry) signals during encoding and maintenance of each sequentially presented memory item in a fine time scale; (3) correlating cognitive load and individual differences in working memory to neural and peripheral physiology, and studying the relationship between the physiological signals; (4) integration of the physiological findings with the vast knowledge coming from behavioral studies of verbal working memory in simple span paradigms.

EEG, pupillometry, ECG and photoplethysmography, and behavioral data are stored separately in corresponding folders. Each data record can consist of four data folders: beh - behavioral data: correctness of the recall in the memory trials ecg - electrocardiography (ECG) and photoplethysmography (PPG) data eeg - EEG data pupil - pupillometry and eye-tracking data

Some of the participants had some physiological data missing: sub-017, sub-094 have no pupillometry data sub-017, sub-037, sub-066 have no ECG and PPG data sub-013, sub-014, sub-015, sub-016, sub-017, sub-018, sub-019, sub-020, sub-021, sub-022, sub-023, sub-024, sub-025, sub-026, sub-027, sub-028, sub-029, sub-030, sub-031, sub-037, sub-066 have no EEG data

The MIMIC PERform datasets are a series of datasets extracted from the MIMIC III Waveform Database. Each dataset contains recordings of physiological signals from critically-ill patients during routine clinical care. Specifically, the datasets contain the following signals:

• electrocardiogram (ECG)
• photoplethysmogram (PPG)
• impedance pneumography (imp), also known as respiratory (resp)

Further details of the datasets are provided in the documentation accompanying the ppg-beats project, which is available at: https://ppg-beats.readthedocs.io/en/latest/ . In particular, documentation is provided on the following datasets:

• MIMIC PERform AF Dataset: Recordings from 35 critically-ill adults during routine clinical care, categorised as either AF (atrial fibrillation, 19 subjects) or non-AF (16 subjects).
  • Matlab format (all data, adults, neonates)
  • WFDB format (all data, adults, neonates)
  • CSV format (all data, adults, neonates)
• MIMIC PERform Training Dataset: Recordings from 200 patients during routine clinical care, who are categorised as either adults (100 subjects) or neonates (100 subjects).
  • Matlab format (all data, adults, neonates)
  • WFDB format (all data, adults, neonates)
  • CSV format (all data, adults, neonates)
• MIMIC PERform Testing Dataset: Recordings from 200 patients during routine clinical care, who are categorised as either adults (100 subjects) or neonates (100 subjects).
  • Matlab format (AF subjects, non-AF subjects)
  • WFDB format (AF subjects, non-AF subjects)
  • CSV format (AF subjects, non-AF subjects)

Each dataset is accompanied by a licence which acknowledges the source(s) of the data - please see the individual licenses for these acknowledgements.

453 Global import shipment records of Ppg with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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.

Dataset AssociationThis dataset belongs to the project "PPG Signals and Cholesterol Data: Repository for the Validation of Total Blood Cholesterol Estimation Methods" where different PPG signals are presented together with cholesterol information of the subjects. This is done with the objective of validating tools or methods for estimating the total blood cholesterol level from the PPG signal.Dataset DescriptionThis dataset contains files in .csv (Comma-separated values) format, corresponding to the PPG signal of 46 subjects. Subject data such as age, sex, and cholesterol are not found in the files presented here. If these data are needed in the records, they can be located in the following dataset within this project "PPG Signals & Cholesterol Data Subject Files (Format: .csv)". Other data such as weight, height and whether the subject is on medication can be found in the excel document included in the project.Dataset format.csv (Comma-separated values)Other formats available in the project:.txt (Text file).json (JavaScript Object Notation).mat (MATLAB file)

9362 Global import shipment records of Ppg with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

shreyanbr/PPG-data-for-ML dataset hosted on Hugging Face and contributed by the HF Datasets community