Security Framework for Pervasive Healthcare Architectures Utilizing MPEG-21 IPMP Components

Nowadays in modern and ubiquitous computing environments, it is imperative more than ever the necessity for deployment of pervasive healthcare architectures into which the patient is the central point surrounded by different types of embedded and small computing devices, which measure sensitive physical indications, interacting with hospitals databases, allowing thus urgent medical response in occurrences of critical situations. Such environments must be developed satisfying the basic security requirements for real-time secure data communication, and protection of sensitive medical data and measurements, data integrity and confidentiality, and protection of the monitored patient's privacy. In this work, we argue that the MPEG-21 Intellectual Property Management and Protection (IPMP) components can be used in order to achieve protection of transmitted medical information and enhance patient's privacy, since there is selective and controlled access to medical data that sent toward the hospital's servers

On the detection of myocardial scar based on ECG/VCG analysis

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%)

Detection of myocardial scar from the VCG using a supervised learning approach

This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the in- vestigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue

A combined cyber and physical attack resilience scheme for Health Services Critical Infrastructure

This work focuses on proposing the basic components of a resilience scheme that can be used for the protection of Health Services Critical Infrastructure (HSCI) and the protection of its key assets based on combined protection against cyber and physical attacks

A combined cyber and physical attack resilience scheme for Health Services Critical Infrastructure

This work focuses on proposing the basic components of a resilience scheme that can be used for the protection of Health Services Critical Infrastructure (HSCI) and the protection of its key assets based on combined protection against cyber and physical attacks

Accelerated SuFEx Click Chemistry for Modular Synthesis

Click chemistry is a method for the rapid synthesis of functional molecules with desirable properties. We report the development of accelerated SuFEx, a powerful click reaction for the efficient coupling of aryl and alkyl alcohols directly with SuFExable hubs catalyzed by 2-tert-butyl-1,1,3,3-tetramethylguanidine (BTMG, Barton\u27s base). The new method circumvents the need to synthesize silyl ether substrates while allowing the use of sub-stoichiometric catalyst loadings. This is made possible through a synergistic effect between BTMG and hexamethyldisilazane (HMDS) additive. The powerful combination drives the in situ formation of reactive TMS-ether intermediates while exploiting the silicon-fluoride bond formation\u27s thermodynamic driving force. Comparatively, the required BTMG base\u27s catalyst loading is generally low (1.0–20 mol%) compared to the dominant SuFEx catalyst, DBU (10–30 mol%). In line with click chemistry principles, the scalable reaction only requires simple evaporation of the volatile side products (NH3, Me3Si-F, TMS-OH, BTMG) under reduced pressure instead of extensive purification. The new SuFEx protocol is tolerant of a wide selection of functional groups and meets all the demands of a click reaction, thereby dramatically shortening reaction times and delivering products in excellent yield

Towards a resilient health status assessment employing intelligence in cyber physical systems

Resilience is defined as the capacity of a system to cope with a hazardous event or disturbance, responding or reorganizing in ways that maintain its essential function, identity, and structure, while also maintaining the capacity for adaptation, learning and transformation. A resilient health system is one that is capable to anticipate, respond to, cope with, recover from and adapt to system-related shocks and stress, so as to bring sustained improvements in population health, despite the unstable circumstances. Nowadays, the emergency department (ED) of hospitals faces growing demands, rising acuteness and longer waiting times. An efficient, accurate and resilient triage system to improve the operation of the ED becomes crucial. In this work, a resilient system for automatic priority, sorting and monitoring of medical events -triage system- is developed, so that the patients in the ED are treated according to the severity of their condition and not by the order of attendance utilizing a Fuzzy Inference System (FIS) that aggregates, processes patients’ vital signs as well as determines their Health Status (HS), according to which the ED staff performs the appropriate classification

Identification of Heart Arrhythmias by Utilizing a Deep Learning Approach of the ECG Signals on Edge Devices

Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, such as in hospital emergency departments. The proposed lightweight solution uses a novel classifier, consistently designed and implemented, based on a 2D convolutional neural network (CNN) and properly optimized in terms of storage and computational complexity, thus making it suitable for deployment on edge devices capable of operating in hospital emergency departments, providing privacy, portability, and constant operation. The experiments on the MIT-BIH arrhythmia database, show that the proposed 2D-CNN obtains an overall accuracy of 95.3%, mean sensitivity of 95.27%, mean specificity of 98.82%, and a One-vs-Rest ROC-AUC score of 0.9934. Moreover, the results and metrics based on the NVIDIA® Jetson Nano™ platform show that the proposed method achieved excellent performance and speed, and would be particularly useful in the clinical practice for continuous real-time (RT) monitoring scenarios