فصلنامه مهندسی پزشکی زیستی
سال یازدهم شماره 2 (تابستان 1396)

To increase the number of stimulation frequencies in the Steady-state visual evoked potential (SSVEP)-based brain-computer interface, we are forced to broaden the frequency range due to the frequency resolution restriction. This will enter frequencies with harmonic relation into the stimulation frequency range and lead to increase in frequency recognition error. In this paper, a three-stage method including the empirical mode decomposition (EMD), the canonical correlation analysis (CCA) and neural network classifier has been proposed that can solve the recognition error problem for wide frequency range including frequencies with harmonic relation. Visual stimulus ranged from 6-16 Hz with an interval of 0.5 have been generated using Matlab and the psychophysics toolbox. The SSVEP signal was recorded from ten subjects via one electrode placed at Oz. After extracting the intrinsic mode functions (IMFs) of the signal by EMD and reconstructing the combined signals, the CCA has been applied. Two features including the detected frequency and the correlation value in this frequency have been extracted and they were given to the neural network classifier. For eight-second time window, the average accuracy of the CCA for N=1 was 78% and for N=2 was 74%, while the corresponding values of the proposed method were 82% and 77% respectively. For four-second time window, the accuracy was increased from 78% to 83% for N=1 and it was increased from 78% to 80% for N=2. N is the number of harmonics in the generation of the reference signal in the CCA. For wide frequency range, the proposed method has been able to improve the frequency recognition accuracy compared to the standard CCA method. according to this, by broadening the stimulation frequency range, the possibility of increasing the number of frequency options and thus increasing the information transfer rate are provided.

In this study, a portable upperlimb exoskeleton is designed for the purpose of rehabilitation and helping the disabled people to do their daily activities. This exoskeleton has two active and one passive degrees of freedom for the shoulder joint. In this system, the idea of cable transmossiom mechanism and guidance pulley with the ability to change the force direction is used. The two active DOFs of the shoulder joint is achived only by pulling one cable for each axis based on the novel design of this mechanism. Each axis of the shoulder is driven independently which implies that a single axis can be installed on the arm. This exoskeleton does not impose any limitation on the vertical motion of the scapula. In addition, it is inexpensive, lightweight and can easily be used. In this paper, after introducing the exoskeleton system, the required motor torques for generating a prescribed task are obtained. In the next step, the kinematic and dynamic equations of this system are derived. By simulating the exoskeleton in CATIA and MATLAB softwares, and presenting the results, the performance of the exoskeleton is evaluated. The results show that this novel exoskeleton system posses an excellent capacity to perform the rehabilitation excersises for shoulder joint.

Neural function depends on the received synapses and the intrinsic properties of the neuron. However, synaptic integration and intrinsic responses can largely depend on the synaptic inputs. In this respect, deep cerebellar nuclei (DCN) neurons which receive inhibitory synapses from Purkinje cells (PCs) are of interest. Transmission of behavior from PC to DCN in awake animal and how this information is coded by the deep cerebellar nuclei remain unknown. To investigate this issue, simultaneous recordings from about 50 Purkinje cells converged to each DCN is required, which is impossible in experiments. Therefore, it is required to use modeling techniques. In this study, to explore the effect of Purkinje cells inputs on the power spectral of DCN output, the transmission of behavioral information from the Purkinje cell to the DCN, and behavior coding by the DCN, artificial spike trains (ASTs) of the Purkinje cell were generated, and behavioral modulation (respiration) was added to them, then, ASTs were applied to the DCN model. Power spectral density analysis of the DCN firing in response to the synaptic inputs from Purkinje cells was made and the frequency bands of the DCN output were analyzed. Results showed that the behavioral modulation frequency is reflected in the DCN spectrum and a peak is visible at low-frequencies at the power spectral of the DCN output in response to the behavioral modulation received from Purkinje cells. On the other hand, the previous study has shown that DCN performs frequency coding in response to the behavioral modulation received from Purkinje cells. Results of the present study coud confirm the frequency coding by the DCN. In addition, a high-frequency peak was observed, which coud be due to the tonic firing of the DCN.

The atherosclerosis disease is the most prevalent illness that occurs in large or medium size arteries. The most important consequence of this disease is creation of arterial platelets in places where in addition to artery damages; the density of materials such as low density lipoprotein (LDL) is being increased. The produced platelets not only block appropriate blood delivery to downstream fibers but also in advanced stages, rubbing or tearing platelet could bring about clot and eventually heart or brain stroke. In this research, in order to review the procedure of LDLs accumulation within lumens and arterial wall, numerical simulation of LDL particles mass transport by using several layer model and diffusion coefficient depending on shear rate are used. Arteries’ walls are assumed to be porous and rigid. In this study, Navier–Stokes equations, mass transport, and Darsi have been solved by numerical methods with regarding to non-Newtonian behavior of blood in lumens and different layers of vessel’s wall. In this article, the impacts of diffusion coefficient being constant or variable, impact of non-Newtonian behavior of blood, impact of non-Newtonian behavior of plasma and impact of blood pressure on the amount of LDL accumulation in lumen and layers of carotid artery are reviewed. The results indicate that diffusion coefficient variation in arterial lumen and non-Newtonian behavior of plasma within the arterial wall could affect significantly on LDL accumulation. In addition, increasing blood pressure not only increases LDL accumulation on interface of blood and arterial wall but also increases the accumulation within arterial wall layers and consequently the artery is more susceptible to atherosclerosis development.

In terms of mechanical behavior, human’s speaking and generating voice is a sophisticated process which is resulted in interaction between flowing air through the larynx and oscillating functionality of vocal folds. The sulcus vocalis is one of the individual cases of scarring in which the superficial lamina propria is absent over the length of the vocal fold and can procreate several disorders in voice generation. In this study, for the first time, the effects of sulcus vocalis on vibrating functionality of vocal folds have been assessed by employing finite element numerical modeling. Two-dimensional models of either healthy or sulcus vocal folds were implemented which each one is coupled and solved via LS-dyne software. Also, the three e-layer linear elastic model was utilized for the structure phase and the arbitrary Lagrangian-Eulerian (ALE), incompressible continuity, and Navier- Stokes relations were used for the fluid domain. Type II patients’ self-excited oscillations have been exhibited and compared with the healthy model. The results of the healthy model were assessed and compared with numerical and experimental results of previous studies. Moreover, the influences of the sulcus not only on the flow components but also on the oscillating functionality of the vocal folds have been evaluated. The results indicated that the frequency of vocal folds’ vibrations and the value of volume flux tends to be remarkably declined and boosted up respectively.

Autism spectrum disorder (ASD) is a common disorder among children which despite painstakingly effort, it is not yet possible to be precisely detected using paraclinical methods. On the other hand, early detection, before 18th month, has pivotal role in treatment procedure. In this study, we present a method for early diagnosis of ASD based on the qualitative analysis of the Electroencephalogram (EEG) signal. We develop a new domain for quantifying the quality of interaction is present. We name it 'stretching – folding space’ (SFS). This domain is based on cybernetics, holistic and information-based analysis approaches. Therefore, it provides a non-deterministic approach to the biosignals. We collected data from 60 normal and 60 children with ASD in the range of 3-10 years old. We extracted features from the data in the SFS domain. The design of the study is self-controlled, meaning that each child serves as his/her own control. Each subject in the study watched a cartoon with and without sound, and the EEG signals were recorded. Statistical tests are applied on the extracted qualitative features in the SFS domain. The difference between the features of the data for each group (normal and ASD) was extracted, and the difference were compared between the groups. The results indicate that there is a statistically significant difference between the SFS features of normal and autism children. We conclude that our proposed method can serve as a new signal processing tool for diagnosing autism.

Sudden cardiac death (SCD) is one of the most significant and common causes of heart related deaths around the world. It is believed that SCD can be predicted using signatures and features extracted from ECG signal. These signatures may be seen as arrhythmia or abnormalities in the ECG signal. In this paper, a monitoring index is introduced for early detection of SCD. This index is acquired by filtering the ECG signal using a nonlinear ECG dynamical model and extended Kalman filter (EKF). The nonlinear dynamical model was a modified version of polar ECG dynamical model proposed by Mc. Sharry et.al. In our algorithm, first the ECG dynamical model is extracted. Then an EKF is applied on the signal. Using the fidelity index extracted from the innovation signal yielded by EKF, a novel algorithm detects the SCD related arrhythmias and abnormalities. The proposed method was evaluated on Physionet Sudden Cardiac Death Holter database. Twenty records corresponding to patients having SCD and eighteen records corresponding to healthy patients were extracted from this database. The evaluation results showed that our proposed monitoring index correctly detected 17 SCDs out of 20 (85% accuracy).