Journal of Medical Signals and Sensors
Volume:2 Issue: 1, Jan-Mar 2012

In recent decade، the research regarding wireless applications in e-Health services has increased. Simple communications، fast delivery of medical information، reducing treatment cost and reducing the medical workers error rate، are the main benefits of using wireless technologies in e-Health applications. But on the other hand using wireless technologies causes the electromagnetic interference (EMI) problem. Although power management and reducing transmission power is an effective method to avoid the EMI، but it causes the number of successful message deliveries to the AP to decreases، and hence، the quality of service (QoS) requirements cannot be meet. In this paper، we first introduce QoS requirements in different medical data transmissions، and then two approaches for power management in sensitive healthcare environments are presented and its effect on the interference and outages are investigated. In the rest of this paper، we propose the use of relays for decreasing the probability of outage in aforementioned scenario. Relay placement is the main factor to enjoy the usefulness of relay station benefits in network، so we use the genetic algorithm to compute the optimum positions of a fixed number of relays. We have considered delay and maximum blind point coverage as two main criteria in relay station problem. Finally the performance of proposed method is investigated.

This paper describes the system design of a portable and economical mu rhythm based Brain Computer Interface which employs Cypress Semiconductors Programmable System on Chip (PSoC). By carrying out essential processing on the PSoC، the use of an extra computer is eliminated، resulting in considerable cost savings. Microsoft Visual Studio 2005 and PSoC Designer 5. 01 are employed in developing the software for the system، the hardware being custom designed. In order to test the usability of the BCI، preliminary testing is carried out by training three subjects who were able to demonstrate control over their electroencephalogram by moving a cursor present at the center of the screen towards the indicated direction with an average accuracy greater than 70% and a bit communication rate of up to 7 bits/min.

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection، in which theso-called Gray-Level Difference (GD) and Average Background Difference (ABD) are considered as the inputs of a Wavelet Network (WN). Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise witha median filter. The Wavelet Network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively، and at the same time preserves chromaticity and image details very well.

This paper represents a new region-based active contour model that can be used to segment images with intensity non-uniformity and high level noise. The main idea of our proposed method is to use Gaussian distributions with different means and variances with incorporation of intensity non-uniformity model for image segmentation. In order to integrate the spatial information between neighboring pixels in our proposed method، we use Markov Random Field. Our experiments on synthetic images and simulated cerebral MR images show the advantages of the proposed method.

As it has proven the increase of mechanical strain could resulted to increase of BNP (protein brain natriuretic peptide) in blood stream of implanted patient pacemakers. We measured the BNP concentration in blood due to different mode and lead implantation location of pacemaker in the time period of three month. The aim of study was the investigation of change trend of BNP level after pacemaker implantation. One hundred and three pacemaker implanted patients were monitored. Patients were in age span of 54±12 including 48 men and 55 women. A group of 44 programmed in DDDR mode and a group of 59 of them programmed in VVIR mode by the recommendation of cardiologist. Between these two groups the pacing levels of pacemakers divided to under and above fifty percent. Some of these pacemaker leads were located at apex of the right ventricle and the others were located in septum wall in right ventricle. To evaluate BNP change during a period of three month the BNP’s measured in pg/ml(pictogram/ milliliter) within 24 hours of implantation(BNP1) and after three month(BNP2). For different class of pacemaker implantations the ratio of final measurement (BNP2) is divided to after implantation measurements (BNP1). Consensus result are showing that in VVIR mode the ratio is 1.54±0.3 and in DDDR mode the ratio is 0.38±0.17 with acceptable standard error means(<0.04). Also comparisons are made for lead location at two modes of DDDR and VVIR separately. In DDDR mode the ratio for apex location is 0.49±0.12 and for septum location is 0.22±0.34 with acceptable standard error means(<0.02). In VVIR mode the ratio for apex location is 1.71±0.27 and for septum location is 1.28±0.09 with acceptable standard error means(<0.04). Therefore BNP decrease in DDDR mode is more than VVIR mode programming. In both cases of DDDR and VVIR modes the septum location of the leads would result to more decrease of BNP.

In this paper we propose novel algorithms for retrieving dental images from databases by their contents. Based on special information of dental images، for better content based dental image retrieval and representation، the image attributes are used. We propose DISR (Dental Image Segmentation and Retrieval)، a content-based image retrieval method which is robust to translation and scaling of the objects in the images. A novel model is used to calculate the features of the image. We implemented the dentition plaster casts and proposed a special technique for segmenting teeth in our dental study models. For testing the efficiency of the presented algorithm a software system is developed، and 60 dental study models are used. The models are covering different kinds of malocclusions. Our experiments show that 95% of extracted results are accurate and the presented algorithm is efficient.

Image classification is an issue which utilizes image processing، pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification and it expected to be more developed in the future. Due to this fact that automatic diagnosis which use intelligent methods such as medical image classification can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of adaptive neuro-fuzzy inference system (ANFIS) as classifier in medical image classification during the past 16 years. A brief comparison with other classifier، main advantages and drawbacks of this classifier are mentioned. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of a FIS with the learning power of artificial neural networks (ANNs). The objective of ANFIS is to integrate the best features of fuzzy systems and neural network.

Understanding the genetic regulatory networks، the discovery of interactions between genes، and understanding regulatory processes in a cell at the gene level، is one of the major goals of system biology and computational biology. Modeling gene regulatory networks، describing the actions of the cells at the molecular level and is used in medicine and molecular biology applications such as metabolic pathways and drug discovery. Modeling these networks is also one of the important issues in Genomic Signal Processing. After the advent of microarray technology، it is possible to model these networks using time-series data. In this paper، we provide an extensive review of methods that have been used on time-series data and represent features، advantages and disadvantages of each. Also، we classify these methods according to their nature. A parallel study of these methods can lead to the discovery of new synthetic methods or improve previous methods.