An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID‐19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region‐specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co‐occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID‐19 infection. The proposed algorithm was compared with other existing state‐of‐the‐art deep neural networks using the Radiopedia and COVID‐19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance‐alignment measure (EM_φ), and structure measure (S_m) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID‐19 infection with limited datasets.     

Remote health monitoring system for detecting cardiac disorders

Remote health monitoring system with clinical decision support system as a key component could potentially quicken the response of medical specialists to critical health emergencies experienced by their patients. A monitoring system, specifically designed for cardiac care with electrocardiogram (ECG) signal analysis as the core diagnostic technique, could play a vital role in early detection of a wide range of cardiac ailments, from a simple arrhythmia to life threatening conditions such as myocardial infarction. The system that the authors have developed consists of three major components, namely, (a) mobile gateway, deployed on patient''s mobile device, that receives 12‐lead ECG signals from any ECG sensor, (b) remote server component that hosts algorithms for accurate annotation and analysis of the ECG signal and (c) point of care device of the doctor to receive a diagnostic report from the server based on the analysis of ECG signals. In the present study, their focus has been toward developing a system capable of detecting critical cardiac events well in advance using an advanced remote monitoring system. A system of this kind is expected to have applications ranging from tracking wellness/fitness to detection of symptoms leading to fatal cardiac events.Inspec keywords: cardiovascular system, diseases, patient monitoring, electrocardiography, medical signal processing, medical signal detection, electric sensing devices, medical disordersOther keywords: remote health monitoring system, cardiac disorder detection, clinical decision support system, cardiac care, electrocardiogram signal analysis, core diagnostic technique, cardiac ailments, arrhythmia, myocardial infarction, life threatening conditions, mobile gateway, patient mobile device, ECG signals, ECG sensor, remote server component, point‐of‐care device, fatal cardiac events     

Controllability of fractional dynamical systems with distributed delays in control using ψ-Caputo fractional derivative

This research investigates the controllability of linear and non-linear fractional dynamical systems with distributed delays in control using the $\psi$-Caputo fractional derivative. For controllability of linear systems, the positive definiteness of Grammian matrix, which is characterized by Mittag–Leffler functions, is used to provide necessary and sufficient conditions. For the controllability of non-linear systems, the iterative technique with the completeness of $X$ is used to obtain sufficient conditions. Using the $\psi$-Caputo fractional derivative, this study is new since it investigates the ideas of controllability. A couple of numerical results are offered to explain the theoretical results.