Becalm: Intelligent Monitoring of Respiratory Patients

Abstract

The Becalm project is an open and low-cost solution for the remote monitoring of respiratory support therapies like the ones used in COVID-19 patients. It is a combined architecture based on a Case-Based Reasoning (CBR) decision-making system for the remote monitoring, detection, and explanation of risk situations for respiratory patients using a low-cost non-invasive mask. This paper describes the mask and the sensors that allow remote monitoring. Then, it describes the intelligent decision-making system that detects anomalies and raises early alerts that are visualized and explained to healthcare professionals. This detection is based on the comparison of cases that represent patients using a set of static variables, plus the dynamic vector of the patient time series from sensors. The experiments reported in this paper are based on a synthetic data generator that simulates realistic patients using a synthesis process developed from the analysis of the available clinical literature. This process has been verified with real data and allows the validation of the reasoning system with noisy and incomplete data, threshold values, and life/death situations. Besides, we have evaluated three different distance metrics for the reasoning system in either optimal situations or cold-start and noisy situations. Our results demonstrate promising results and good accuracy for the proposed low-cost method to supervise COVID-19 patients for medical staff.