Biomedical Engineering: Applications, Basis and Communications

The main objective of the current study was to evaluate differences between autonomic responses of monolinguals and Turkish- and Kurdish-bilinguals, while pictorial stimuli with four emotional contents, including happy, relax, sad and fear was presented. The galvanic skin responses (GSR), electrocardiogram (ECG) and pulse signal (PS) of 21 college students, including seven Persian monolinguals, seven Kurdish–Persian, and seven Turkish–Persian bilinguals were collected. Estimating the maximum value of the power spectral density and some nonlinear features including Lyapunov exponents (LE), Sample entropy (SaEn), and Lempel–Ziv (LZ) complexity, significant differences were examined by means of statistical analysis. Heightened autonomic responses were found for maximum power and LZ of bilinguals in comparing to monolinguals. A reverse pattern was revealed for other measures. Another important finding was the significant differences between bilinguals and monolinguals autonomic indices; however, no significant differences within two groups of bilinguals were observed. It can be concluded that the autonomic responses to pictorial stimuli can reveal emotional differences of monolingual and bilingual individuals. The results of the current study will be of interest to the researchers working in affective sciences, psycholinguistics, and cross-cultural psychology.

NMR is one of the important analytic tools which is used to obtain certain information such as metabolic concentrations in neural or muscular tissues. In some other important applications such as proton decoupling, it is necessary to design NMR transmitters/receivers capable of operating at multiple frequencies, while maintaining a good performance at each frequency. In this work, a new nuclear magnetic resonance (NMR) receiver microcoil based on MEMS technology is proposed. The designed structure uses MEMS microswitches with low contact resistance and NMR-based actuation mechanism. The proposed device can detect carbon (13C), proton (1H), and phosphorus (31p) nucleus with larmor frequencies of 96.36MHz, 383MHz, and 155.11MHz at 9 T magnetic field, respectively. The designed microcoil achieves three important goals:

• (1)Getting high SNR, high Q and high filling factor which are key parameters in NMR performance, by changing number of turns.
• (2)Turning into the array of microcoils to obtain better SNR.
• (3)Turning into two or three microcoils inside of each other for simultaneous detection.

The MEMS microswitch in this paper uses static magnetic field of the NMR for its operation ($B_0=9$T) which simplifies the switch mechanism. This switch is small (150$\mu$m$\times$50$\mu$m$\times$6$\mu$m), scattering parameters of 43.2db isolation and 0.0059 insertion loss and maximum displacement of 2.03$\mu$m due to the magnetostatic actuation. In this work, the models and investigations are conducted using finite element simulations in COMSOL Multiphysics. The switch scattering parameters are obtained by HFSS 12.0.

Background: The purpose of this study is to investigate the prevalence of neck and low back discomfort and its association with intervertebral angulations of cervical and lumbar during neutral and flexion among aircrews.

Methods: There were 283 subjects participating in this survey. All participants finished one questionnaire each regarding complaints about musculoskeletal symptoms, and three sagittal plane radiographs. The measurements of intervertebral angulations were completed using the Cobb method for lumbar spine and the Harrison posterior tangent method for the cervical spine.

Results: In the questionnaire, 26.5% subjects self-reported neck discomfort and 33.2% subjects self-reported low back discomfort. Based on categorization by self-reported complaints, there was significant decrease in two intervertebral flexion angles of the Discomfort group than those of the Regular group, including C3/C4 and C2/C7. Based on categorization by radiological diagnoses, the abnormal group revealed significant decreases in the regular range of motion from neutral to flexion at C3/C4 segment. Lordotic angles between L1 and L5 were also found to be significantly different between the Discomfort group and the Regular group.

Conclusions: A high prevalence of neck and low back discomfort among the aircrews is revealed and needs more suitable intervention. A certain level of association between spinal discomfort and the intervertebral angulations of the lumbar spine and the cervical spine in aircrews was found when compared to the normal group.

This is a study on measuring the effect of stress that stimulated by the different difficulty of game level on pupil dilation. Playing games are claimed a method to distress in the community, however, this activity is enhancing stress level? The effect of the game playing on the changes of pupil dilation is unknown. An investigation was conducted on relating the changes of pupil diameter measurement at the different difficulty levels of the game. A simple tool was developed by using a webcam as image acquisition device together with MATLAB Image Processing Tool to analyze the acquired images. Here, experiments on the pupil detection algorithm, which consisted of four different methods are to work alternatively with one another and to provide higher chances of pupil detection. 10 subjects were involved in the experiments, which they were requested to play a selected game with four different ascending increased in difficulties, meanwhile, pupil size changes were measured simultaneously. The selected game served as a stress stimulus to the subjects. The result showed a positive proportional relationship between increased in game difficulty levels and pupil dilation. The investigation reported that the pupil dilated when the difficulty of game level increased. Limitation of this study is the sample size on the prototype testing should be increased to concrete the outcome.

Forecasting of an epileptic seizure and localization of the epileptogenic region is a challenging task. Scalp electroencephalogram (EEG) is the most commonly used signal for studying various brain disorders. This paper presents an algorithm for seizure forecast and detection of epileptogenic region by analyzing EEG signals from frontal, temporal, central and parietal region of the brain. Eight features have been extracted from each EEG signal. Average of features extracted from different regions of brain is computed for each region. An artificial neural network is trained to predict an epileptic seizure by identifying the pre-ictal duration. The trained neural network is tested and found to have an accuracy of 92.3%, sensitivity of 100% and specificity, of 83.3%. Two prominent features, accumulated energy and power in beta band, have been identified to identify the epileptogenic region. The result shows that the region corresponding to temporal lobe has maximum variation in these two features for pre-ictal and inter-ictal duration. The result validates the proposed algorithm to identify the pre-ictal state and predict the seizure in advance and identification of the epileptogenic region.

Human action recognition (HAR) is a challenging problem because of the complexity and similarity in different actions. In recent years, many methods have been proposed for HAR. Sparse coding-based approaches have been widely used in this field. Also, many works have been done based on manifold learning theory. When the videos are similar but from different classes, their sparse codes may be similar and the actions might be classified mistakenly. In this paper, a multi-modal affine graph regularized sparse coding approach is proposed for solving this problem in HAR. At first, HOG3D, HOG/Hof and SURF3D descriptors were extracted from the action datasets, then the sparse codes have been obtained for each descriptor using the proposed method. The dictionary learning method used in this step has more discrimination power in respect to the traditional methods. Then, these codes are scored differently using SVM classifier and at last a Naïve Bayes leads to a final decision. Experiments on KTH, Weizmann and UCF Sport action datasets show that the proposed method can significantly outperform several previous methods in human action classification especially in real-world data.

In the course of aligning medical images, the similarity metric (also called alignment function) is regarded as the objective function, and the optimization method as the tool for exploring the optimal transformation parameters. In this paper, the medical image alignment is represented first and then the optimization methods are depicted in detail. With this description, by the use of the mutual information (MI) as the similarity metric, the Powell method and the particle swarm optimization (PSO) method are employed to seek the optimal transformation parameters respectively, and their optimization performances are estimated and compared. The experimental results show that the Powell and PSO methods can cater to both the multi-modality medical image alignments.

Nowadays, magnetic resonance imaging (MRI) has been widely applied for diagnosis of soft-tissue diseases. Most clinical MRI protocols use fat suppression (FS) methods to suppress fat signal, reduce chemical shift artifacts, and increase conspicuity of lesions. To understand the advantages, disadvantages, and clinical applications of the most commonly used FS methods is an important issue. The aim of this study was to evaluate FS performance of six FS methods on a fat-water phantom at 1.5T. The six MRI methods included iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL), short inversion time inversion recovery (STIR), and four chemical presaturation (Chem Presat) methods. The phantom was composed of homogeneous oil-in-water emulsions with various fat contents ranging from 0 to 100% in increments of 10%. The difference between the suppressed fat fractions (FS fractions) and the true fat fractions of the phantom was used as an index of FS performance. The correlations and levels of agreement (LOAs) between the FS fractions determined using each FS method and the true fat fractions of the phantom were analyzed. From the phantom study, it was found that FSE T2 FS, STIR and IDEAL could achieve more accurate FS fractions than the other three methods. The FS fractions determined using FSE T2 FS, STIR and IDEAL were in a good agreement. On the contrary, T2-weighted spin echo Chem Presat had the most inaccurate quantification of FS fractions among these six FS methods. Both the ranks of signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the phantom were IDEAL $>$ FSE T2 FS $>$ STIR. The FS performance of these six FS methods in clinical use needs further study.