A Real-Time Remote Monitoring System for Cardiovascular Diseases

Currently, real-time recording and bio-signal-based early diagnosis are feasible solutions thanks to increasing progress in monitoring device development technology, including self-monitoring devices, integrated electronic systems, the Internet of Things, and edge computing. The pandemic emergency of coronavirus disease 2019 (COVID-19) activated the remote monitoring era and highlighted the need for innovative digital approaches to managing cardiovascular disease. The scientific community and health organizations have considered this new era confirming that remote consultation and monitoring systems have become indispensable in cardiovascular healthcare circumstances to enhance patient healthcare and offer personalized treatment. The paper aims to introduce a real-time remote monitoring system for cardiovascular diseases and to describe the proposed system modules and the ECG signal processing algorithms. The described approach can monitor the patient's cardiac activity, allowing the specialist to control the electronic instruments remotely without leaving their office. Therefore, this system aims at all cardiopathic patients with objective motor difficulties either because they are bedridden or geographically located in places distant from the health facility of interest. Furthermore, considering the real-time monitoring approach of this system, a future application scenario in a global pandemic context can be hypothesized. © 2022 Seventh Sense Research Group®

Crowd change detection from VHR images acquired by UAV via deep features exploitation

Images acquired by RGB cameras on Unmanned Aerial Vehicles (UAVs) can be particularly useful to detect crowd in urban areas when restrictive conditions are imposed for the sake of public safety or health, such as during the Covid 19 pandemic. Together with acquired images, opportune pattern recognition techniques have to be considered to extract useful information. In this framework, features capturing the semantic rich information encompassed in VHR images have to be computed. In particular, Deep Neural Networks (DNN) have been recently proved to be able to extract useful features from data [1]. Moreover, in a transfer learning approach, a DNN pre-trained on a data set can be used to extract opportune features, named deep-features, from another data set, belonging to a different applicative domain [1], [2]. Here, a transfer learning technique is presented to produce change maps, detecting how people gathering increases, from VHR images. It is based on deep-features computed by using some pre-trained convolutional layers of AlexNet. The proposed methodology has been tested on a data set composed of several synthetic VHR images, that simulate crowd collecting in a park, as they can be acquired by RGB camera on a UAV flying at 10 meters height from the ground. The experimental results show that the proposed technique is able to efficiently detect change due to new people incoming in the scene or people that get away, with a low computational cost and in a near-real time operative mode. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Cloud-based biomedical system for remote monitoring of ALS patients

The adoption of telemedicine services during the pandemic for people affected by chronic diseases is crucial to guarantee access of these people to health care services avoiding the risk of COVID-19 infection. This paper illustrates an innovative medical monitoring and teleconsultation cloud-based system for ALS (Amyotrophic Lateral Sclerosis) people. The system consists of two integrated modules. The first module aims to visualize and analyze electromyographic (EMG), electrocardiographic (ECG), and electroencephalographic (EEG) signals. A second module deals with the storage of digital data using a cloud-based web platform. © 2020 IEEE.

Vitamin D deficiency as a predictor of poor prognosis in patients with acute respiratory failure due to COVID-19.

PURPOSE: Hypovitaminosis D is a highly spread condition correlated with increased risk of respiratory tract infections. Nowadays, the world is in the grip of the Coronavirus disease 19 (COVID 19) pandemic. In these patients, cytokine storm is associated with disease severity. In consideration of the role of vitamin D in the immune system, aim of this study was to analyse vitamin D levels in patients with acute respiratory failure due to COVID-19 and to assess any correlations with disease severity and prognosis. METHODS: In this retrospective, observational study, we analysed demographic, clinical and laboratory data of 42 patients with acute respiratory failure due to COVID-19, treated in Respiratory Intermediate Care Unit (RICU) of the Policlinic of Bari from March, 11 to April 30, 2020. RESULTS: Eighty one percent of patients had hypovitaminosis D. Based on vitamin D levels, the population was stratified into four groups: no hypovitaminosis D, insufficiency, moderate deficiency, and severe deficiency. No differences regarding demographic and clinical characteristics were found. A survival analysis highlighted that, after 10 days of hospitalization, severe vitamin D deficiency patients had a 50% mortality probability, while those with vitamin D ≥ 10 ng/mL had a 5% mortality risk (p = 0.019). CONCLUSIONS: High prevalence of hypovitaminosis D was found in COVID-19 patients with acute respiratory failure, treated in a RICU. Patients with severe vitamin D deficiency had a significantly higher mortality risk. Severe vitamin D deficiency may be a marker of poor prognosis in these patients, suggesting that adjunctive treatment might improve disease outcomes.