The wide adoption of pulse oximeters has given clinicians an easy, non-invasive way to measure arterial oxygen saturation. However, evidence suggests that pulse oximeter measurements have a deeper discrepancy in patients of darker skin tones compared to their lighter counterparts. It has been hypothesized that skin tone is the root cause of this phenomenon. However, skin tone as a medical concept has not been extensively studied in acute care.

Our study included patients admitted to Duke University Hospital with pulse oximetry recorded up to 5 minutes prior to arterial blood gas (ABG) measurements. Skin tone was measured across sixteen body locations using administered visual scales (Fitzpatrick, Monk Skin Tone, and Von Luschan), reflectance colorimetry (Delfin SkinColorCatch), and reflectance spectrophotometry (Konica Minolta CM-700D, Variable Spectro 1). IPhone SE 2020 and Google Pixel 4 (Android) image data are available for non-biometric body locations.

One hundred twenty-eight patients are enrolled in this study. A total of 167 skin tone variables and two temperature variables are collected per body location, excluding images, together with ten non-biometric body location images per patient and the associated electronic health record (EHR) data. The ENCoDE project is a comprehensive EHR-linked skin tone database to combat skin tone associate disparities.

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.

This dataset of electrodermal activity was collected from 11 healthy volunteer subjects who were awake and at rest in seated position and 11 different healthy volunteers who were under controlled propofol sedation. For the awake and at rest subjects, the activity was recorded from each subject's non-dominant hand for one hour at 256 Hz. For the controlled propofol sedation subjects, the activity was recorded from each subject's left hand for about 3-4 hours at 500 Hz. From the raw data, EDA pulses were extracted and the pulse times and amplitudes reported. Electrodermal activity measures changing electrical conductance of the skin as an indicator of sweat gland activity. Sweat glands are a primitive part of the fight-or-flight response. These data were collected as part of a larger study to understand and build computational models for autonomic nervous system activity (including electrodermal activity) with approval from the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects (COUHES) and the Massachusetts General Hospital Human Research Committee.

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

The goal of this dataset is to estimate respiratory 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](http://doi.org/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](http://doi.org/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.

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

The goal of this dataset is to estimate blood oxygen saturation level 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.

ALOTT is a pilot project that gathered telemetry data from 270 beds and over 15,000 hospital admissions from September 2018 through November 2020 at The James Cancer Hospital and Ross Heart Hospital. ALOTT contains telemetry waveforms, such as electrocardiogram and blood oxygen, at 60-240Hz and temperature, pulse, and perfusion measurements at two-second intervals linked to Electronic Medical Record (EMR) data. These EMR data include patient admissions, demographics, diagnosis, history, orders, labs, medications, allergies, and nurse flowsheet chart events, including vitals, risk scores, ventilator use, and emergency response team (ERT) events. This dataset was constructed to facilitate the creation of algorithms to predict ERT events.

This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification.

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.

Relevant Papers:

M. Kachuee, M. M. Kiani, H. Mohammadzade, M. Shabany, Cuff-Less High-Accuracy Calibration-Free Blood Pressure Estimation Using Pulse Transit Time, IEEE International Symposium on Circuits and Systems (ISCAS'15), 2015.

A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J.Mietus, G. Moody, C. Peng and H. Stanley, “Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals,†Circulation, vol. 101, no. 23, pp. 215–220, 2000.

Citation Request:

If you found this data set useful please cite the following:

M. Kachuee, M. M. Kiani, H. Mohammadzade, M. Shabany, Cuff-Less High-Accuracy Calibration-Free Blood Pressure Estimation Using Pulse Transit Time, IEEE International Symposium on Circuits and Systems (ISCAS'15), 2015.

M. Kachuee, M. M. Kiani, H. Mohammadzadeh, M. Shabany, Cuff-Less Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring, IEEE Transactions on Biomedical Engineering, 2016.

The CPS dataset, assembled at Klinikum Esslingen, Germany, encompasses 113 diagnostic polysomnographic sleep recordings. These recordings include up to 36 raw and 23 derived data channels, alongide 81 types of annotated events for each participant, supplemented by data from various questionnaires. The dataset was collected during 2021-2022 for the medical study "Computer-aided diagnostics of sleep-related arousals on the basis of pulse wave analyses" in a collaborative effort between Klinikum Esslingen, IT-Designers Gruppe and NRI Medizintechnik GmbH, all based in Germany. The dataset may be used for research that is in-line with the original study goals (see more in section "Usage Notes").