Journal of Medical Signals and Sensors
Volume:7 Issue: 3, Jul-Sep 2017

Cortical spatiotemporal signal patterns based on object recognition can be discerned from visualstimulation. These are in the form of amplitude modulation (AM) and phase modulation (PM) patterns,which contain perceptual information gathered from sensory input. A high-density Electroencephalograph(EEG) device consisting of 48 electrodes with a spacing of 5mm was utilized to measure frontallobe activity in order to capture event-related potentials from visual stimuli. Four randomized stimulirepresenting different levels of salient responsiveness were measured to determine if mild stimuli can bediscerned from more extreme stimuli. AM/PM response patterns were detected between mild and moresalient stimuli across participants. AM patterns presented distinct signatures for each stimulus. AMpatterns had the highest number of incidents detected in the middle of the frontal lobe. Through thiswork, we can expand our encyclopedia of neural signatures to object recognition, and provide a broaderunderstanding of quantitative neural responses to external stimuli. The results provide a quantitativeapproach utilizing spatiotemporal patterns to analyze where distinct AM patterns can be linked to objectperception.

Independent component analysis (ICA) has been used for detecting and removing the eye artifactsconventionally. However, in this research, it was used not only for detecting the eye artifacts, but also fordetecting the brain-produced signals of two conceptual danger and information category words. In thiscross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode.In the first part of this research, the statistical community test case included four men and four women,who were 25–30 years old. In the designed task, three groups of traffic signs were considered, in whichtwo groups referred to the concept of danger, and the third one referred to the concept of information. Inthe second part, the three volunteers, two men and one woman, who had the best results, were chosenfrom among eight participants. In the second designed task, direction arrows (up, down, left, and right)were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from theback of the head; however, in the thinking times, they were different. According to this result, alphawaves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and itwas at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger andinformation signals were well classified; these differences for the 5/8 volunteers were observed in theright hemisphere, and, for the other three volunteers, the differences were observed in the lefthemisphere. For the second task, simulations showed that the best classification accuracies resultedwhen the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neuralnetwork except hardlim discriminator function, the classification accuracies were almost the same andnot very different. Linear discriminant analysis (LDA) in comparison with the neural network yieldedhigher classification accuracies. ICA is a suitable algorithm for recognizing of the word’s concept and itsplace in the brain. Achieved results from this experiment were the same compared with the results fromother methods such as functional magnetic resonance imaging and methods based on the brain signals(EEG) in the vowel imagination and covert speech. Herein, the highest classification accuracy wasobtained by extracting the target signal from the output of the ICA and extracting the features ofcoefficients AR model with time interval of 2.5 s. Finally, LDA resulted in the highest classificationaccuracy more than 60%.

In X-ray computed tomography (CT), the presence of metal objects in a patient’s body producesstreak artifacts in the reconstructed images. During the past decades, many different methods wereproposed for the reduction or elimination of the streaking artifacts. When scanning a patient, theprojectiondata affected by metal objects (missing projections) appear as regions with high intensities in thesinogram. In spiral fan beam CT, these regions are sinusoid-like curves on sinogram. During the firsttime, if the metal curves are detected carefully, then, they can be replaced by correspondingunaffected projections using other slices or opposite views; therefore, the CT slices regenerated bythe modified sonogram will be imaged with high quality. In this paper, a new method of thesegmentation of metal traces in spiral fan–beam CT sinogram is proposed. This method is based on asinogram curve detection using a curvelet transform followed by 2D Hough transform. The initialenhancement of the sinogram using modified curvelet transform coefficients is performed bysuppressing all the coefficients of one band and applying 2D Hough transform to detect moreprecisely metal curves. To evaluate the performance of the proposed method for the detection ofmetal curves in a sinogram, precision and recall metrics are calculated. Compared with othermethods, the results show that the proposed method is capable of detecting metal curves, with betterprecision and good recovery.

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organizationof the brain using hemodynamic responses. In this method, volume images of the entire brain areobtained with a very good spatial resolution and low temporal resolution. However, they always sufferfrom high dimensionality in the face of classification algorithms. In this work, we combine a supportvector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification byusing SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM forfeature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension ofdata sets for having less computational complexity and (ii) it is useful for identifying brain regions withsmall onset differences in hemodynamic responses. Our non-parametric model is compared withparametric and non-parametric methods. We use simulated fMRI data sets and block design inputsin this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets.fMRI simulated dataset has contrast 1–4% in active areas. The accuracy of our proposed method is93.63% and the error rate is 6.37%.

Wireless body area networks consist of several devices placed on the human body, sensing vital signsand providing remote recognition of health disorders. Low power consumption is crucial in thesenetworks. A new energy-efficient topology is provided in this paper, considering relay and sensor nodes’energy consumption and network maintenance costs. In this topology design, relay nodes, placed on thecloth, are used to help the sensor nodes forwarding data to the sink. Relay nodes’ situation is determinedsuch that the relay nodes’ energy consumption merges the uniform distribution. Simulation results showthat the proposed method increases the lifetime of the network with nearly uniform distribution of therelay nodes’ energy consumption. Furthermore, this technique simultaneously reduces network maintenancecosts and continuous replacements of the designer clothing. The proposed method alsodetermines the way by which the network traffic is split and multipath routed to the sink.

After total hip arthroplasty, there would be some problems for the patients. Implantloosening is one of the significant problems which results in thigh pain and even revision surgery.Difference between Young’s modulus of bone-metal is the cause of stress shielding, atrophy, andsubsequent implant loosening.
In this paper, femoral stem stiffness is reducedby novel biomechanical and biomaterial design which includes using proper design parameters, coatingit with porous surface, and modeling the sketch by the software. Parametric design of femoral stem isdone on the basis of clinical reports.
Optimized model for femoral stem is proposed. Curvedtapered stem with trapezoidal cross-section and particular neck and offset is designed. Fully poroussurface is suggested. Moreover, Designed femoral stem analysis showed the Ti6Al4V stem which iscovered with layer of 1.5mm in thickness and 50% of porosity is as stiff as 77 GPa that is 30% less thanthe stem without any porosity. Porous surface of designed stem makes it fix biologically; thus, prosthesisloosening probability decreases.
By optimizing femoral stem geometry (size and shape)and also making a porous surface, which had an intermediate stiffness of bone and implant, a moreefficient hip joint prosthesis with more durability fixation was achieved due to better stress transmissionfrom implant to the bone.

Nowadays, high-intensity focused ultrasound (HIFU) as nonionizing radiation is used for cancertreatment. Basically, the function of HIFU is similar to conventional ultrasound. Ultrasound beamsare perverted when crossing the border of different environments. This decreases the beam’s focuswithin the tumor and may induce damage to the normal tissues. In this study, we aim to developappropriate algorithms for correcting the focal point displacement duced by the beam’s refraction. First,the level of displacement due to difference in two specific tissues was calculated for one element of thetransducer and, then, it extended to all of the elements. Finally, a new focal point was calculated, which isconsidered as a desired focal point of the transducer in which the maximum temperature occurs.Designed algorithms were implemented in MATLAB software. A HIFU simulator (by the Food andDrug Administration of US) was used to simulate HIFU therapy. The proposed algorithm was tested onfour models with two layers of tissue. Results illustrated the use of proposed algorithm results for 78%correction in the focal point displacement. In addition, it was noted that a part of this displacement wascaused by the absorption of the beam in the tissues. The proposed algorithm can significantly correct thefocal point displacement in HIFU therapy and consequently prevent damage to the normal tissues.

Rapid advances in biochemistry and genetics lead to expansion of the various medical instruments fordetection and prevention tasks. On the other hand, food safety is an important concern which relates tothe public health. One of the most reliable tools to detect bioparticles (i.e., DNA molecules and proteins)and determining the authenticity of food products is the optical ring resonators. By depositing a recipientpolymeric layer of target particle on the periphery of an optical ring resonator, it is possible to identifythe existence of molecules by calculating the shift in the spectral response of the ring resonators. Themain purpose of this paper is to investigate the performance of two structures of optical ring resonators,(i) all-pass and (ii) add-drop resonators for sensing applications. We propose a new configuration forsensing applications by introducing a nanogap in the all-pass ring resonator. The performance of theseresonators is studied from sensing point of view. Simulation results, using finite difference time domainparadigm, revealed that the existence of a nanogap in the ring configuration achieves higher amount ofsensitivity; thus, this structure is more suitable for biosensing applications.