Node Status Algorithm for Load Balancing in Distributed Service Architectures at Paperless Medical Institutions

Service architectures are necessary for providing value-added services in telecommunications networks, including those in medical institutions. Separation of service logic and control from the actual call switching is the main idea of these service architectures, examples include Intelligent Network (IN), Telecommunications Information Network Architectures (TINA), and Open Service Access (OSA). In the Distributed Service Architectures (DSA), instances of the same object type can be placed on different physical nodes. Hence, the network performance can be enhanced by introducing load balancing algorithms to efficiently distribute the traffic between object instances, such that the overall throughput and network performance can be optimised. In this paper, we propose a new load balancing algorithm called “Node Status Algorithm” for DSA infrastructure applicable to electronic-based medical institutions. The simulation results illustrate that this proposed algorithm is able to outperform the benchmark load balancing algorithms—Random Algorithm and Shortest Queue Algorithm, especially under medium and heavily loaded network conditions, which are typical of the increasing bandwidth utilization and processing requirements at paperless hospitals and in the telemedicine environment.     

On the association between circulating biomarkers and atherosclerotic calcification in a cohort of arterial disease participants

Background and aimsAtherosclerotic calcification is a powerful predictor of cardiovascular disease. This study aims to determine whether circulating levels of a local/systemic calcification inhibitor or a marker of bone formation correlate with measures of coronary or extracoronary calcification.Methods and resultsClinical computed tomography (CT) was performed on 64 arterial disease participants undergoing carotid and lower extremity endarterectomy. Coronary artery calcium (CAC) scores and volumes were acquired from the CT scans (n = 42). CAC scores and volumes were used to derive CAC density scores. Micro-CT was performed on excised carotid (n = 36) and lower extremity (n = 31) plaques to quantify the volume and volume fraction of extracoronary calcification. Circulating levels of dephospho-uncarboxylated Matrix Gla Protein (dp-ucMGP), fetuin-A, carboxylated and uncarboxylated osteocalcin (ucOC) were quantified using commercial immunoassays. Carotid participant CAC density scores were moderately negatively correlated with plasma dp-ucMGP (r_s = ?0.592, P = 0.008). A weak negative association was found between CAC scores and %ucOC for all participants (r_s = ?0.335, P = 0.040). Another weak negative correlation was observed between fetuin-A and the volume of calcification within excised carotid specimens (r_s = ?0.366, P = 0.031). Despite substantial differences in coronary and extracoronary calcium measurements, the levels of circulating biomarkers did not vary significantly between carotid and lower extremity subgroups.ConclusionCorrelations identified between circulating biomarkers and measures of coronary and extracoronary calcium were not consistent among participant subgroups. Further research is required to determine the association between circulating biomarkers, coronary and extracoronary calcium.     

Prediction Models for Early Risk Detection of Cardiovascular Event

Cardiovascular disease (CVD) is the major cause of death globally. More people die of CVDs each year than from any other disease. Over 80% of CVD deaths occur in low and middle income countries and occur almost equally in male and female. In this paper, different computational models based on Bayesian Networks, Multilayer Perceptron, Radial Basis Function and Logistic Regression methods are presented to predict early risk detection of the cardiovascular event. A total of 929 (626 male and 303 female) heart attack data are used to construct the models. The models are tested using combined as well as separate male and female data. Among the models used, it is found that the Multilayer Perceptron model yields the best accuracy result.     

Crossmodal transfer of emotion by music

Music is one of the most powerful elicitors of subjective emotion, yet it is not clear whether emotions elicited by music are similar to emotions elicited by visual stimuli. This leads to an open question: can music-elicited emotion be transferred to and/or influence subsequent vision-elicited emotional processing? Here we addressed this question by investigating processing of emotional faces (neutral, happy and sad) primed by short excerpts of musical stimuli (happy and sad). Our behavioural experiment showed a significant effect of musical priming: prior listening to a happy (sad) music enhanced the perceived happiness (sadness) of a face irrespective of facial emotion. Further, this musical priming-induced effect was largest for neutral face. Our electrophysiological experiment showed that such crossmodal priming effects were manifested by event related brain potential components at a very early (within 100 ms post-stimulus) stages of neuronal information processing. Altogether, these results offer new insight into the crossmodal nature of music and its ability to transfer emotion to visual modality.     

Improving Diagnostic Quality of MR Images Through Controlled Lossy Compression Using SPIHT

This paper attempts to improve the diagnostic quality of magnetic resonance (MR) images through application of lossy compression as a noise-reducing filter. The amount of imaging noise present in MR images is compared with the amount of noise introduced by the compression, with particular attention given to the situation where the compression noise is a fraction of the imaging noise. A popular wavelet-based algorithm with good performance, Set Partitioning in Hierarchical Trees (SPIHT), was employed for the lossy compression. Tests were conducted with a number of MR patient images and corresponding phantom images. Different plausible ratios between imaging noise and compression noise (ICR) were considered, and the achievable compression gain through the controlled lossy compression was evaluated. Preliminary results show that at certain ICR’s, it becomes virtually impossible to distinguish between the original and compressed–decompressed image. Radiologists presented with a blind test, in certain cases, showed preference to the compressed image rather than the original uncompressed ones, indicating that under controlled circumstances, lossy image compression can be used to improve the diagnostic quality of the MR images.     

Segment-growing Hierarchical Model for Bile Duct Detection in MRCP

Cholangiocarcinoma, cancer of the bile ducts, is often diagnosed via magnetic resonance cholangiopancreatography (MRCP). Due to low resolution, noise and difficulty is actually seeing the tumor in the images, especially by examining only a single image, there has been very little development of automated systems for cholangiocarcinoma diagnosis. This paper presents a computer-aided diagnosis (CAD) system for the automated preliminary detection of the tumor using a single MRCP image. The multi-stage system employs algorithms and techniques that correspond to the radiological diagnosis characteristics employed by doctors. A popular artificial neural network, the multi-layer perceptron (MLP), is used for decision making to differentiate images with cholangiocarcinoma from those without. The test results achieved was 94% when differentiating only healthy and tumor images, and 88% in a robust multi-disease test where the system had to identify the tumor images from a large set of images containing common biliary diseases.     

Cost Effective Patient Location Monitoring System Using Webcams

This paper details the development of a simple webcam joystick, a wireless, or rather cableless, and contactless pointing device by using a webcam and a simple flexible non-electronic joystick. Such a system requires no power source on the joystick, allows for light, robust and very mobile joysticks, and can be extended into a large array of applications. This paper proposes the use of small webcam joysticks as sensors for recording movement, the way wireless sensors are used. Specifically, it could be used as a simple navigation and monitoring system for patient movement in medical wards, where knowledge of patient location and movement could provide instant assistance, pre-emptive action and also hinder untoward patient mix-ups. Experiments and discussions in this paper highlight how a successful implementation is possible, and emphasize the flexibility of such an implementation in a low cost medical environment.