Black Box Testing with Exploratory Approach of a Software for Remote Monitoring of Patients with COVID-19 and Other Infectious Diseases

Covid-19 pandemic has set the world on fire. It has impacted every aspect of our society. One of the worst affected parts is the countries' health systems. Our goal is to provide a proof of concept for cost effective telemedicine system which can be used by hospitals for monitoring of thousands of patients at once, thus medical personnel exposure to the virus is minimized. In this paper we aim to put this system through the best practices of software testing so we can determine if it achieves its primary functions, keeping in line with the general idea of cost effectiveness. Black box testing with the exploratory approach was used for functionality and feature testing as it gives the most accurate real-world results. During the tests, several bugs were found and fixed. Because of the fact, that the testers were actual medical personnel, we have received valuable information on how to improve the quality of the product, so it can be even more ease to use and provide the necessary robustness and data visualization.

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram.

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.