The formulation of a Personal Area Network (PAN), consisting of a wireless infrastructure of medical sensors, attached to patient's body, and a supervising device carried by them, lays the path for continuous and real-time monitoring of vital signs without discomforting the person in question. This infrastructure enhances the context of remote healthcare services by supporting flexible acquisition of crucial vital signs, while at the same time it provides more convenience to the patient. Aiming at the exploitation of the inherent features and requirements of wireless medical sensor networks, in this paper we focus on the main design guidelines of a low power Medium Access Control (MAC) protocol, designated to support a patient PAN. The proposed protocol intends to improve energy efficiency in such applications and thus is oriented towards the prevention of main energy wastage sources, such as collision, idle listening and power outspending.
Abstract. Early detection is the key to improve breast cancer prognosis. The only proven effective method of breast cancer early detection is mammography. An early sign of 30-50% of breast cancer is the appearance of clusters of fine, granular microcalcifications and 60-80% of breast carcinomas reveal microcalcification clusters upon histological examination. The high correlation between the appearance of the microcalcification clusters and diseases, proves that computer aided diagnosis (CAD) systems for automated classification of microcalcification clusters will be very useful and helpful for breast cancer control. The fuzzy nature of microcalcification, the low contrast and the low ability of distinguishing them from their surroundings make automated characterization of them extremely difficult. In this study, we give an overview of the currently available literature on characterization of malignant and benign microcalcifications. We compare and evaluate some of the classification algorithms on microcalcifications in mammograms used in various CAD systems, which are separated into categories according to the method in use. Neural networks are used in applications where only a few decisions are required concerning an amount of data. The K-nearest neighbour classifier distinguishes unknown patterns based on the similarity to known samples and the decision tree approach is much simpler than neural networks and does not need extensive knowledge of the probability distribution of the features.
The continuous treatment of chronic diseases such as heart diseases, pulmonary disease and diabetes, relies on the patient's self-monitoring. The rapid advance in the development of telemedicine systems and monitoring devices during last years gave several solutions in continuous patient telemonitoring. However, in most of the cases, these systems work with only one or a few types of medical devices and, thus, the variety of diseases they can monitor is rather limited. One of the main reasons for this is the fact that medical device manufacturers usually develop their own proprietary communication protocols and data format for each device. Most of the existing telemonitoring systems support communication with limited medical device types and depend on the manufacturer of them. We describe a modular and ambulatory telemedicine platform, the e-Vital platform, where different monitoring devices are being integrated in homecare and telemonitoring service chain, in order to increase patients' quality of life and their feeling of safety concerning their health.
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