Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms.

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level-SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

Benefits of Manometer in Non-Invasive Ventilatory Support.

Introduction Effective ventilation during cardiopulmonary resuscitation (CPR) is essential to reduce morbidity and mortality rates in cardiac arrest. Hyperventilation during CPR reduces the efficiency of compressions and coronary perfusion. Problem How could ventilation in CPR be optimized? The objective of this study was to evaluate non-invasive ventilator support using different devices. METHODS: The study compares the regularity and intensity of non-invasive ventilation during simulated, conventional CPR and ventilatory support using three distinct ventilation devices: a standard manual resuscitator, with and without airway pressure manometer, and an automatic transport ventilator. Student's t-test was used to evaluate statistical differences between groups. P values <.05 were regarded as significant. RESULTS: Peak inspiratory pressure during ventilatory support and CPR was significantly increased in the group with manual resuscitator without manometer when compared with the manual resuscitator with manometer support (MS) group or automatic ventilator (AV) group. CONCLUSION: The study recommends for ventilatory support the use of a manual resuscitator equipped with MS or AVs, due to the risk of reduction in coronary perfusion pressure and iatrogenic thoracic injury during hyperventilation found using manual resuscitator without manometer. Lacerda RS , de Lima FCA , Bastos LP , Vinco AF , Schneider FBA , Coelho YL , Fernandes HGC , Bacalhau JMR , Bermudes IMS , da Silva CF , da Silva LP , Pezato R . Benefits of manometer in non-invasive ventilatory support. Prehosp Disaster Med. 2017;32(6):615-620.