Emergency TeleOrthoPaedics m-health system for wireless communication links

For the first time, a complete wireless and mobile emergency TeleOrthoPaedics system with field trials and expert opinion is presented. The system enables doctors in a remote area to obtain a second opinion from doctors in the hospital using secured wireless telecommunication networks. Doctors can exchange securely medical images and video as well as other important data, and thus perform remote consultations, fast and accurately using a user friendly interface, via a reliable and secure telemedicine system of low cost. The quality of the transmitted compressed (JPEG2000) images was measured using different metrics and doctors opinions. The results have shown that all metrics were within acceptable limits. The performance of the system was evaluated successfully under different wireless communication links based on real data

A Review of Error Resilience Techniques in Video Streaming

Abstract-Delivering video data of satisfactory quality over unreliable networks -such as the internet or wireless networks -is a demanding area which has received significant attention of the research community over the past few years. Given the fact that packet loss is inevitable and therefore the presence of errors granted, the effort is directed towards limiting the effect of these errors. A number of techniques have been developed to address this issue. This paper aims to summarize the most significant approaches for: error resilience, error concealment and joint encoder-decoder error control techniques, and to provide a thorough discussion of the benefits and drawbacks of these error control methods. Furthermore, two case studies of error resilience utilization are presented, namely Ad-hoc networks and Multimedia Broadcast Multiple Services (MBMS)

Analysis of neuromuscular disorders using statistical and entropy metrics on surface EMG

This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, myopathic or neuropathic, regarding to the acquired EMG signals. 39 subjects in total participated in the experiment, 19 normal, 11 myopathic and 9 neuropathic. Surface EMG was recorded using 4-channel surface electrodes on the biceps brachii muscle at isometric voluntary contractions. The recording time was only 5 seconds long to avoid muscle fatigue, and contractions at fiveforce levels were performed, i.e. 10, 30, 50, 70 and 100 % of maximal voluntary contraction. The feature extraction routine deployed the wavelet transform and calculation of the Shannon entropy across all the scales in order to obtain a feature set for each subject. Subjects were classified regarding the extracted features using three machine learning techniques, i.e. decision trees, support vector machines and ensembles of support vector machines. Four 2-class classifications and a 3-class classification were performed. The scored classification rates were the following: 64+-11% for normal/abnormal, 74+-7% for normal/myopathic, 79+-8% for normal/neuropathic, 49+-20% for myopathic/neuropathic, and 63+-8% for normal/myopathic/neuropathic

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's disease: A Methodological Review

Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging especially in Mild Cognitive Impairment (MCI) subjects. Quantitative structural Magnetic Resonance Imaging (MRI) acquisition methods in combination with Computer-Aided Diagnosis (CAD) are currently being used for the assessment AD. These acquisitions methods include: i) Voxel-based Morphometry (VBM), ii) volumetric measurements in specific Regions of Interest (ROIs), iii) cortical thickness measurements, iv) shape analysis and v) texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following 3 groups: Normal Controls (NC), MCI and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the Medial Temporal Lobe (MTL) especially in the entorhinal cortex, whereas with disease progression both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus

Virtual Reality for Pain Management in Cancer:A Comprehensive Review

Virtual Reality is a computer-simulated 3-Dimensional technology in which the user interacts via different senses: visual, auditory, tactile, and/or olfactory. In the past decades, it has been argued that Virtual Reality as a technique could be applied in the clinical environment to successfully manage pain. This article provides a systematic review of research on Virtual Reality and pain management for patients who are suffering from cancer. More specifically, this article focuses on all types of Virtual Reality technologies (Non-Immersive, Semi-Immersive, Fully-Immersive) which has been developed and released to manage the pain which evokes from the treatment of cancer. An exhaustive search identified 23 relevant studies from 2010 to 2020. Overall, the identified studies indicated that Virtual Reality can improve the experience of pain for patients who are suffering from cancer. It was also found that, if Virtual Reality is appropriately designed, the pain which is arising from cancer treatments can be reduced. Even though some positive outcomes have been reported, overall, the results are inconclusive and studies that examine specifically the treatment of pain in cancer patients are limited. Further research needs to be conducted, to articulate clearly, under what circumstances Virtual Reality is an effective tool for cancer patients, and under what factors Virtual Reality can be the solution to the pain patients are experiencing.</p

A feasibility study for the provision of electronic healthcare tools and services in areas of Greece, Cyprus and Italy

Background: Through this paper, we present the initial steps for the creation of an integrated platform for the provision of a series of eHealth tools and services to both citizens and travelers in isolated areas of thesoutheast Mediterranean, and on board ships travelling across it. The platform was created through an INTERREG IIIB ARCHIMED project called INTERMED. Methods: The support of primary healthcare, home care and the continuous education of physicians are the three major issues that the proposed platform is trying to facilitate. The proposed system is based on state-of-the-art telemedicine systems and is able to provide the following healthcare services: i) Telecollaboration and teleconsultation services between remotely located healthcare providers, ii) telemedicine services in emergencies, iii) home telecare services for "at risk" citizens such as the elderly and patients with chronic diseases, and iv) eLearning services for the continuous training through seminars of both healthcare personnel (physicians, nurses etc) and persons supporting "at risk" citizens. These systems support data transmission over simple phone lines, internet connections, integrated services digital network/digital subscriber lines, satellite links, mobile networks (GPRS/3G), and wireless local area networks. The data corresponds, among others, to voice, vital biosignals, still medical images, video, and data used by eLearning applications. The proposed platform comprises several systems, each supporting different services. These were integrated using a common data storage and exchange scheme in order to achieve system interoperability in terms of software, language and national characteristics. Results: The platform has been installed and evaluated in different rural and urban sites in Greece, Cyprus and Italy. The evaluation was mainly related to technical issues and user satisfaction. The selected sites are, among others, rural health centers, ambulances, homes of "at-risk" citizens, and a ferry. Conclusions: The results proved the functionality and utilization of the platform in various rural places in Greece, Cyprus and Italy. However, further actions are needed to enable the local healthcare systems and the different population groups to be familiarized with, and use in their everyday lives, mature technological solutions for the provision of healthcare services

Unsupervised level set parameterization using multi-scale filtering

This paper presents a novel framework for unsupervised level set parameterization using multi-scale filtering. A standard multi-scale, directional filtering algorithm is used in order to capture the orientation coherence in edge regions. The latter is encoded in entropy-based image `heatmaps', which are able to weight forces guiding level set evolution. Experiments are conducted on two large benchmark databases as well as on real proteomics images. The experimental results demonstrate that the proposed framework is capable of accelerating contour convergence, whereas it obtains a segmentation quality comparable to the one obtained with empirically optimized parameterization

AM-FM Texture Image Analysis of the Intima and Media Layers of the Carotid Artery

Abstract. The purpose of this paper is to propose the use of amplitude modulation-frequency modulation (AM-FM) features for describing atherosclerotic plaque features that are associated with clinical factors such as intima media thickness and a patient&apos;s age. AM-FM analysis reveals the instantaneous amplitude (IA) of the media layer decreases with age. This decrease in IA maybe attributed to the reduction in calcified, stable plaque components and an increase in stroke risk with age. On the other hand, an increase in the median instantaneous frequency (IF) of the media layer suggests the fragmentation of solid, large plaque components, which also lead to an increase in the risk of stroke. The findings suggest that AM-FM features can be used to assess the risk of stroke over a wide range of patient populations. Future work will incorporate a new texture image retrieval system that uses AM-FM features to retrieve intima and intima media layer images that could be associated with the same level of the risk of stroke