Biosignal Databases for Training of Artificial Intelligent Systems

Abstract

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will have mild to moderate respiratory diseases, however, the elderly population is the most vulnerable, becoming seriously ill, requiring continuous medical follow-up. In this sense, technologies were developed that allow continuous and individual monitoring of patients, in a home environment, namely through wearable devices, thus avoiding continuous hospitalization. Thus, these devices allow great improvements in data analysis methods since they can continuously acquire the physiological signals of an individual and process them in real-time through artificial intelligence (AI) methods. However, training of AI methods is not straightforward, requiring a large amount of data. In this study, we review the most common biosignal databases available in the literature. A total of thirteen databases were selected. Most of the databases (9 databases) were related to ECG signal, as well as 4 databases containing signals from SPO2, Heart Rate, Blood Pressure, etc. Characteristics were described, namely: the population of the databases, data resolution, sampling rates, sample time, number of signal samples, annotated classes, data acquisition conditions, among other aspects. Overall, this study summarizes and described the public biosignals databases available in the literature, which may be important in the implementation of intelligent classification methods.