Photoplethysmography (PPG)

The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is a large, de-identified and publicly-available collection of medical records. Each record in the dataset includes ICD-9 codes, which identify diagnoses and procedures performed. Each code is partitioned into sub-codes, which often include specific circumstantial details. The dataset consists of 112,000 clinical reports records (average length 709.3 tokens) and 1,159 top-level ICD-9 codes. Each report is assigned to 7.6 codes, on average. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Source: MIT Laboratory for Computational Biology

We introduce here a new database called UBFC-rPPG (stands for Univ. Bourgogne Franche-Comté Remote PhotoPlethysmoGraphy) comprising two datasets that are focused specifically on rPPG analysis. The UBFC-RPPG database was created using a custom C++ application for video acquisition with a simple low cost webcam (Logitech C920 HD Pro) at 30fps with a resolution of 640x480 in uncompressed 8-bit RGB format. A CMS50E transmissive pulse oximeter was used to obtain the ground truth PPG data comprising the PPG waveform as well as the PPG heart rates. During the recording, the subject sits in front of the camera (about 1m away from the camera) with his/her face visible. All experiments are conducted indoors with a varying amount of sunlight and indoor illumination. The link to download the complete video dataset is available on request. A basic Matlab implementation can also be provided to read ground truth data acquired with a pulse oximeter.

The MMSE-HR benchmark consists of a dataset of 102 videos from 40 subjects recorded at 1040x1392 raw resolution at 25fps. During the recordings, various stimuli such as videos, sounds, and smells are introduced to induce different emotional states in the subjects. The ground truth waveform for MMSE-HR is the blood pressure signal sampled at 1000Hz. The dataset contains a diverse distribution of skin colors in the Fitzpatrick scale (II=8, III=11, IV=17, V+VI=4).

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

The Multi-domain Mobile Video Physiology Dataset (MMPD), comprising 11 hours(1152K frames) of recordings from mobile phones of 33 subjects. The dataset was designed to capture videos with greater representation across skin tone, body motion, and lighting conditions. MMPD is comprehensive with eight descriptive labels and can be used in conjunction with the rPPG-toolbox and PhysBench. MMPD is widely used for rPPG tasks and remote heart rate estimation. To access the dataset, you are supposed to download this data release agreement and request downloading by email.

Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the Vision for Vitals dataset was introduced. It is a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population. It contains more than 150+ subjects with over 1300+ videos along with ground truth heart rate and respiration rate annotations. It also includes blood pressure waveform signals as part of its physiological data.

BUAA-MIHR dataset is a remote photoplethysmography (rPPG) dataset. BUAA-MIHR dataset for evaluation of remote photoplethysmography pipeline under multi-illumination situations. We recruited 15 healthy subjects (12 male, 3 female, 18 to 30 years old) in this experiment and a total number of 165 video sequences were recorded under various illuminations. The experiments were conducted in a darkroom in order to isolate from ambient light.

The MIMIC PERform Testing dataset contains the following physiological signals recorded from 200 critically-ill patients during routine clinical care: - electrocardiogram (ECG) - photoplethysmogram (PPG) - impedance pneumography (imp), also known as respiratory (resp) - (in some cases) arterial blood pressure (abp) Each signal is sampled at 125 Hz. The dataset also contains some fixed parameters for each subject (such as whether the subject was an adult or neonate). The dataset is available in CSV, Matlab, and WaveForm Database formats here. Further details of the datasets are provided in the documentation accompanying the ppg-beats project documentation. The dataset was extracted from the MIMIC III Waveform Database.

the MTHS dataset contains 30Hz PPG signals obtained from 62 patients, including 35 men and 27 women. The ground truth data includes heart rate and oxygen saturation levels sampled at 1Hz. The HR and SPo2 measurement is obtained using a pulse oximeter (M70). An iPhone 5s was used to obtain the ppg recordings at 30 fps.

a dataset of multi-modal signals from wearable devices at four sites on the body. Each device continuously recorded synchronized signals from a 3-channel reflective photoplethysmogram (red, green, infrared PPG), 3-axis inertial sensor (accelerometer), temperature, and barometric altitude sensor. For reference, the sternum device continuously recorded a Lead-I electrocardiogram (ECG) from body-mounted gel electrodes to provide ground-truth heart rate (HR) estimates.

HYPE Dataset - Version 1.0.0 REFERENCE PAPER ------------------- Morassi Sasso, A., Datta, S., Jeitler, M., Steckhan, N., Kessler, C. S., Michalsen, A., Arnrich, B., & Böttinger, E. (2020). HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population. In M. Michalowski & R. Moskovitch (Eds.), Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science, volume 12299 (pp. 325–335). Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_29. CONTENT ------------------ - Sensor (PPG & Blood Pressure) and clinical data from 9 hypertensive subjects in two experiments (stress test and 24 hours) - PPG data from Empatica E4: photoplethysmography (PPG) data. - Spacelabs (SL 90217): blood pressure data. - Demographics and self-reported data during 24 hours (exercise, medication, etc.). AVAILABILITY ------------------ - Available to the scientific community through a data agreement. - The requester must be affiliated to a research institution.

This is a comprehensive dataset of 6,388 surgical patients composed of intraoperative biosignals and clinical information. The biosignal data included in the dataset is high quality data such as 500 Hz waveform signals and numeric values at intervals of 1-7 seconds. More than 60 surgery related clinical information is also provided to help interpret the signals. The dataset is provided free of charge to help researchers who want to study and develop new medical AI algorithms using monitoring signals from surgical patients. We expect that the distribution of this world's largest biosignal dataset will greatly contribute to the advancement of medical AI research.