Abstract
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 segments 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.
Highlights
Background and SummaryThe incidence of cardiovascular disease (CVD) has risen around the world in recent years overtaking the mortality rate of cancer, making CVD the number one killer of humans
A physiological information database with high precision and a high sampling rate is urgently needed in PPG technology research in order to extract more cardiovascular parameters for the early screening and diagnosis of CVDs
We provide here a database containing physiological information and PPG waveform data collected over a year that can be used to research arterial blood vessel aging, arterial blood pressure detection[8], and screening of hypertensive and diabetic patients based on PPG signals
Summary
The incidence of cardiovascular disease (CVD) has risen around the world in recent years overtaking the mortality rate of cancer, making CVD the number one killer of humans. The openness of the data allows clinical studies to explore and improve the understanding of relationships between cardiovascular health and PPG signals, with the final goal of creating a simple, effective non-invasive detection technology that is easy to use and wearable. This dataset has been collected from 219 subjects, aged 21–86 years, with a median age of 58 years. The dataset covers several diseases including hypertension, diabetes, cerebral infarction, and insufficient brain blood supply This unique non-invasive detection dataset for cardiovascular disease can be used in a wide range of in-depth research. We provide an example of the basic properties of the database that allows researchers to conduct research
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