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.

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.

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.

The performance metric values of the proposed model for PPG, ECG (Channel-1), and ECG (Channel-2) test data.

Photoplethysmogram (PPG) signals collected from nine subjects in a still seated position, measured using Asiawill Pulse Heart Rate sensor implemented in an Arduino-based wearable sensor-kit.

This dataset, 'MARSH', includes respiratory signals as part of the study "Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography." It is meant to support academic research, particularly on algorithm development tools.

Contents: - Data.txt (age, gender, height, weight, systole, diastole, [respectively]) - ECG.mat (raw ECG data) - ECG_annot.mat (annotations for the R peaks in ECG data) - IP.mat (Raw IP data) - IP_annot.mat (annotations for the local maxima of IP data [end of inspiration phase]) - NASAL.mat (Thermistor mask data) - NASAL_annot.mat (annotations for the local maxima of thermistor mask data [end of inspiration phase]) - PPG.mat (Raw PPG signal data)

When referring to this dataset, please consider including the following reference:

Mikko Pirhonen and Vehkaoja Antti, Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography. Biomedical Signal Processing and Control. 2020

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

Photoplethysmograph (PPG) is a physiological signal used to describe the volumetric change of blood flow in peripherals with heart beats. A hardware configuration is employed to capture PPG signals from a number of persons using an IoT sensor. This dataset contains PPG signals from 35 healthy persons , with 50 to 60 PPG signal for each one. Each PPG signal contains 300 samples (6 seconds recording) with 50 sample/second sampling rate. The dataset is split into two files: one for training the ANN which contains 1374 PPG signal (about 66% of complete dataset), and the other file to test the ANN which contains 700 PPG signal (about 34% of complete dataset).

This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (SD=3.8, MIN=1.0, MAX=13.4) from a population of 21 students of which 12 have also completed an exit survey, and 7 started before the California COVID-19 lockdown order. This multimodal dataset included different means of data collection such as Samsung Galaxy Watch, Oura Ring, a Life-logger app named Personicle, a questionnaire mobile app named Personicle Questions, and periodical and personalised surveys. The dataset contains raw data from Photoplethysmogram (PPG), Inertial measurement unit (IMU), and pressure sensors in addition to processed data on heart rate, heart rate variability, sleep (bedtime, sleep stages, quality), and physical activity (step, active calories, type of activity). Ecological momentary assessments were collected from participants on daily and weekly bases containing their ..., Experiment Design and Setup This study was designed to create a longitudinal dataset of physiological and emotional assessments for emerging adults. This dataset can be used for studying affect and correlations of mental health, affect, physiology, sleep, and activity of emerging adults. However, as COVID-19 pandemic and lockdown started during the study, the purpose of this study was shifted toward studying the effects of the lockdown on participants' life and mental health. To adapt to the new conditions, the study design, recruitment materials, and questionnaires were updated as the study was ongoing. This study collected objective ubiquitous data from wearable devices and life-logger apps and combined them with subjective ecological momentary assessments (EMAs) and surveys to create a spectrum of physiological and mental profile for each participant over time. To achieve this objective, this study was required to collect different modalities of data. First, the participants needed ..., , # Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: An intensive, longitudinal dataset

This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (SD=3.8, MIN=1.0, MAX=13.4) from a population of 21 students of which 12 have also completed an exit survey, and 7 started before the California COVID-19 lockdown order. This multimodal dataset included different means of data collection such as Samsung Galaxy Watch, Oura Ring, a Life-logger app named Personicle, a questionnaire mobile app named Personicle Questions, and periodical and personalised surveys. The dataset contains raw data from Photoplethysmogram (PPG), Inertial measurement unit (IMU), and pressure sensors in addition to processed data on heart rate, heart rate variability, sleep (bedtime, sleep stages, quality), and...

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 .txt (Text file) 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: .txt)". 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.txt (Text file)Other formats available in the project:.csv (Comma-separated values).json (JavaScript Object Notation).mat (MATLAB file)

Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in different areas from the norm (extrasystole). Arrhythmias can disrupt the balanced circulation, leading to serious complications like heart attacks, strokes, and sudden death. Medical devices like electrocardiography (ECG) and Holter monitors are commonly used for diagnosing and monitoring cardiac rhythm disorders. However, in recent years, the development of wearable devices has played a significant role in the detection and diagnosis of rhythm disorders through the use of photoplethysmography (PPG) signals. Wearable devices enable patients to continuously monitor their health status and allow doctors to provide earlier diagnoses and interventions. In this study, a 1D-CNN model is proposed to detect arrhythmias using PPG signals. A dataset prepared by the University of Massachusetts Medical Center (UMMC) containing both ECG and PPG signal data was utilized. In this dataset, ECG signals are filtered with a bandpass filter and raw PPG signals are divided into 30-second segments. Accuracy values were obtained by classifying ECG and PPG signals using a 1D CNN model. ECG signals were used as a reference. The proposed model achieved a 95.17% accuracy rate in detecting normal sinus rhythm (NSR), atrial fibrillation (AF), and premature atrial contractions (PAC) from PPG signals. Datasets are available for download on https://www.synapse.org/pulsewatch. The codes used in this study are available on the https://github.com/miraygunay/PPG-Code.git website.

Spandan

  A Large Photoplethysmography (PPG) Signal Dataset of 1 Million+ Indian Subjects

In Sanskrit, "Spandan" (स्पन्दन - spandana) represents one of the most fundamental aspects of existence - the rhythmic pulsation that permeates all life. Derived from the root verb "spand" (स्पन्द), meaning "to throb" or "to pulsate," it beautifully captures the essence of the heartbeat.

  Dataset Overview

Spandan is an extensive repository containing over 1 million… See the full description on the dataset page: https://huggingface.co/datasets/ekacare/spandan-1M-V1.0-raw.

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.

A dataset containing arterial blood pressure (ABP) signals and their corresponding finger photoplestimography (PPG). This dataset is a processed version of the MIMIC-III Waveform Database Matched Subset.

File names were inherited from MIMIC-III. Files are saved in ".mat" and each file contains 2 structures with raw signals and different computed characteristics. Each structure corresponds to 15-second segments sampled at 125Hz.

For more details, please refer to MIMIC-III Waveform Database Matched Subset and the processing source code.

A comparison of studies developed for the detection of cardiovascular diseases from signals obtained from wearable devices with machine learning methods.

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

Aims: This study aims to compare the performance of physicians to detect atrial fibrillation (AF) based on photoplethysmography (PPG), single-lead ECG and 12-lead ECG, and to explore the incremental value of PPG presentation as a tachogram and Poincaré plot, and of algorithm classification for interpretation by physicians.Methods and Results: Email invitations to participate in an online survey were distributed among physicians to analyse almost simultaneously recorded PPG, single-lead ECG and 12-lead ECG traces from 30 patients (10 in sinus rhythm (SR), 10 in SR with ectopic beats and 10 in AF). The task was to classify the readings as ‘SR', ‘ectopic/missed beats', ‘AF', ‘flutter' or ‘unreadable'. Sixty-five physicians detected or excluded AF based on the raw PPG waveforms with 88.8% sensitivity and 86.3% specificity. Additional presentation of the tachogram plus Poincaré plot significantly increased sensitivity and specificity to 95.5% (P < 0.001) and 92.5% (P < 0.001), respectively. The algorithm information did not further increase the accuracy to detect AF (sensitivity 97.5%, P = 0.556; specificity 95.0%, P = 0.182). Physicians detected AF on single-lead ECG tracings with 91.2% sensitivity and 93.9% specificity. Diagnostic accuracy was also not optimal on full 12-lead ECGs (93.9 and 98.6%, respectively). Notably, there was no significant difference between the performance of PPG waveform plus tachogram and Poincaré, compared to a single-lead ECG to detect or exclude AF (sensitivity P = 0.672; specificity P = 0.536).Conclusion: Physicians can detect AF on a PPG output with equivalent accuracy compared to single-lead ECG, if the PPG waveforms are presented together with a tachogram and Poincaré plot and the quality of the recordings is high.

Raw data for imaging photoplethysmogram extraction collected in 10 recording sessions from four rhesus monkeys and Matlab scripts for their processing.For details of the study please refer to [1].INSTRUCTIONS:1. Please download the zip-archive and unpack its contents. 2. Please launch the script dataset_analysis.m in MATLAB to reproduce the results described in [1]. For a detailed description of the code please see [2].Matlab scripts require Matlab Wavelet Toolbox.DATA DESCRIPTION:Data of session N is placed in a folder SessionN. Data for sessions 1-8 consist of four files:SessionN.bin - raw color signal data in binary file, in format r1 g1 b1 r2 g2 ....;SessionN_ROIdata.txt - metadata of ROI used for data color signal computing;SessionN_interframeDif.txt - interframe differences in the video, proxy for the amount of motion. Format: stream of double values with a new line symbol as delimiter, use a = load('SessionN_interframeDif.txt', '-ascii') to load in Matlab;SessionN_REF.csv - average reference pulse rate values for time intervals of length reported in file summary.txt.Data for session 1 additionally contain the following files:Session1RegionN.bin - raw color signal data in binary file for Section 1 with ROI different from region 3; Session1RegionN_ROIdata.txt - metadata of ROI used for data color signal computing for Section 1 with ROI different from region 3;Data for sessions 2IR, 9IR and 10IR consist of three files:SessionN.bin - raw "color" (effectively - monochrome) signal data in binary file, in format i1 i1 i1 i2 i2 i2 ...;SessionN_ROIdata.txt - metadata of ROI used for signal computing;SessionN_REF.csv - average reference pulse rate values for time intervals of length reported in file summary.txt.REFERENCES:[1] Unakafov AM, Möller S, Kagan I, Gail A, Treue S, Wolf F (2018) Using imaging photoplethysmography for heart rate estimation in non-human primates. bioRxiv. doi:10.1101/252403[2] Matlab package "Imaging photoplethysmogram extraction and pulse rate estimation". https://de.mathworks.com/matlabcentral/fileexchange/67527-matlab-package--imaging-photoplethysmogram-extraction-and-pulse-rate-estimation-

Data Set Information: The main goal of this data set is providing clean and valid signals for designing cuff-less blood pressure estimation algorithms. The raw electrocardiogram (ECG), photoplethysmograph (PPG), and arterial blood pressure (ABP) signals are originally collected from the physionet.org and then some preprocessing and validation performed on them. (For more information about the process please refer to our paper) Attribute Information: This database consists of a cell array of matrices, each cell is one record part. In each matrix each row corresponds to one signal channel: 1: PPG signal, FS=125Hz; photoplethysmograph from fingertip 2: ABP signal, FS=125Hz; invasive arterial blood pressure (mmHg) 3: ECG signal, FS=125Hz; electrocardiogram from channel II Note: dataset is splitted to multiple parts to make it easier to load on machines with low memory. Each cell is a record. There might be more than one record per patient (which is not possible to distinguish). However, records of the same patient appear next to each other. N-fold cross test and train is suggested to reduce the chance of trainset being contaminated by test patients.