Sofia-Maria Dima

On the Detection of Myocadial Scar Based on ECG/VCG Analysis

In this paper, we address the problem of detecting the presence of a myocardial scar from the standard electrocardiogram (ECG)/vectorcardiogram (VCG) recordings, giving effort to develop a screening system for the early detection of the 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 the myocardial scar. Two of these methodologies are: 1) the use of a template ECG heartbeat, from records with scar absence coupled with wavelet coherence analysis and 2) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate a support vector machine classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifiers performance. The classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying tenfold 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%).

Performance evaluation of a WSN system for distributed event detection using fuzzy logic

The research field of event detection in realistic WSN environments has attracted a lot of interest, with health monitoring being one of its most pronounced applications. Although efforts related to the healthcare applications exist in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in error prone and limited resource wireless environments. This paper aimed to address this need by porting a Fuzzy Inference System (FIS) to a WSN simulation framework. The considered FIS is implemented on TelosB motes and evaluates the health status of a monitored person, in an energy conserving manner. A distributed implementation of the above FIS is also proposed, comprising an additional contribution of this paper, based on an objective function, attempting to reduce the network congestion and balance the energy consumption between network nodes. This work presents a thorough performance evaluation of the FIS under the distributed and the centralized approach, while varying the communication conditions and highlighting the advantages of the distributed execution of the FIS, leading to packet loss gain and transmission gain up to 67% and 25% respectively. The networking benefits from the distributed approach are reflected to the FIS performance. Respective results and comparative evaluation against Matlab simulations reveal strong dependencies of the applications performance to critical WSN network parameters.

Data merge: A data aggregation technique for wireless sensor networks

The Wireless Sensor Networks (WSNs) have limited power and communication capabilities, combined with the requirement for long network lifetime. To increase it, methods to reduce energy consumption are highly required. To achieve this goal, we study a data aggregation technique without size reduction, i.e. data merge. It is a generic technique, since it is also usable in applications with heterogeneous data and requirements for high accuracy. This study presents the impact of the data merge technique on WSNs applications executed under various realistic data flow scenarios, traffic loads and wait time intervals. Our results show significant reductions in both packet loss and radio energy consumption.

FLOGERA — A fuzzy logic event recognition algorithm in a WSN environment

Wireless Sensors Networks (WSNs) emphasize the necessity of accurate event detection in realistic environments, while health monitoring comprise one of their most pronounced applications. Although respective fuzzy logic systems have been proposed based on vital and environmental sensors, there is a significant lack of investigation on the reliability of such systems when applied in error prone wireless environments. Therefore, this paper aims to address this need by porting an adequate fuzzy inference system (FIS) to a WSN simulation framework. The considered FIS has been validated in previous work and is implemented in a TelosB emulated mote, which evaluates the health status of a monitored person. Such implementation allows the thorough performance evaluation of the FIS under a wide range of communication conditions. Respective results and comparative evaluation against Matlab environment reveal strong dependencies to critical WSN network parameters.

A network reliability oriented event detection scheme for Wireless Sensors and Actors Networks

Wireless Sensors and Actors Networks (WSANs) emphasize the necessity of accurate event detection in order to achieve ubiquitous monitoring in realistic environments and perform appropriate actions on them. One of the most significant applications is health monitoring. This paper is aiming to propose a fuzzy logic system based on multi-sensor vital and environmental data, in order to detect distress situations. Also, a data accuracy fuzzy system is presented in order to estimate the network confidence. Both systems have been evaluated in order to demonstrate their validity and usefulness. The outputs of the aforementioned systems are associated so as to trigger the optimal acting tasks. The proposed data fusion scheme aims at improving the reliability of the system and can be adaptively modified and further enhanced in terms of more sensor readings, number of rules etc.

Priority Handling Aggregation Technique (PHAT) for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have limited power capabilities, whereas they serve applications which usually require specific packets, i.e. High Priority Packets (HPP), to be delivered before a deadline. Hence, it is essential to reduce the energy consumption and to have real-time behavior. To achieve this goal we propose a hybrid technique which explores the benefits of data aggregation without data size reduction in combination with prioritized queues. The energy consumption is reduced by appending data from incoming packets with already buffered Low Priority Packets (LPP). The real-time behavior is achieved by directly forwarding the HPP to the next node. Our study explores the impact of the proposed hybrid technique in several all-to-one data flow scenarios with various traffic loads, wait time intervals and percentage of HPP. Our results show gain up to 23,3% in packet loss and 36,6% in energy consumption compared with the direct forwarding of packets.

Identifying Chronic Disease Complications Utilizing State of the Art Data Fusion Methodologies and Signal Processing Algorithms

In this paper a methodology for identifying patient’s chronic disease complications is proposed. This methodology consists of two steps: a. application of wavelet algorithms on ECG signal in order to extract specific features and b. fusion of the extracted information from the ECG signal with information from other sensors (i.e., body temperature, environment temperature, sweating index, etc.) in order to assess the health state of a monitoring patient. Therefore, the objective of this methodology is to derive semantically enriched information by discovering abnormalities at one hand detect associations and inter-dependencies among the signals at the other hand and finally highlight patterns and provide configuration rulesets for an intelligent local rule engine. The added value of the semantic enrichment process refers to the discovery of specific features and meaningful information with respect to the personalized needs of each patient.

Event Identification in Wireless Sensor Networks

Wireless sensor networks (WSNs) comprise a cornerstone to state-of-the-art and future CyberPhysical systems. Respective networking domain is characterized by a drastically different communication paradigm compared to typical wireless networks offering flexible, dynamically adaptive, low power yet reliable and robust data exchange capabilities. In that context accurate, reliable, and time constrained event detection represents one of the important tasks of a WSN so as to be actually useful and reliable. Therefore in this chapter a comprehensive taxonomy and analysis is attempted of adequate event detection techniques ranging from simple threshold-based to highly sophisticated AI-based approaches specifically targeting WSN networks and CPS applications.

Fuzzy Inference Systems Design Approaches for WSNs

Fuzzy logic represents a well-suited data processing approach for state-of-the-art and future WSN networks utilized in demanding CPS applications. However, efficient execution of such systems require respective optimization approaches taking into consideration specific limitations, requirements, and particularities posed by WSNs networks that are not encountered in typical high resource communication technologies. In this chapter the development of adequate FIS systems targeting is analyzed taking into consideration specific use case scenarios in highly demanding application domains such as health monitoring. Additionally, specific optimization approaches are presented and discussed according to two axes. On one hand, distributed operation through efficient task allocation among WSN nodes. On the other hand different FIS execution strategies and analyzed taking into consideration different resource availability.

Resource Aware Sensor-to-Actor Allocation Framework for WSANs Based on Voronoi Cells Theory

Wireless sensor and actor networks (WSANs) have emerged as a promising research field and have been applied in a wide variety of application domains due to their capability of environment monitoring, event data processing, and decision-making by aiming at performing appropriate actions interacting with the environment. Coordination mechanisms among nodes and actors are a critical research challenge pertaining to the optimum allocation of sensors to a particular actor. Although efforts related to the node-to-actor coordination problem have been presented in the current literature, there is a significant oversight regarding critical characteristics such as the heterogeneous capabilities of the actors as well as the network’s heterogeneous density. In this paper, aiming to address such shortcomings, we introduce the term Actor Service Capacity, which indicates the ability of an actor to serve a particular number of nodes. We also propose a novel node-to-actor coordination algorithm, based on the Voronoi tessellation, aiming to guarantee that the number of nodes, allocated to each actor, will not exceed its capabilities. Furthermore, a set of selection techniques are proposed so as to be applied on the coordination framework. Respective evaluation analysis offers useful conclusions and highlights the importance and the advantages of the proposed algorithm.