فصلنامه مهندسی پزشکی زیستی
سال هشتم شماره 1 (بهار 1393)

In EEG/MEG datasets, the Volume Conduction (VC) artifact appears as instantaneous linear mixing of brain source activities on the channel measurements. A desired characteristic of an ideal EEG/MEGconnectivity estimator (on sensor-space) is its robustness to VC artifact. This means that the VC of independent brain sources must never lead to detection of significant connectivity among EEG/MEG channels. There has been no criterion in the literature so far that can compare the robustness levels of different (sensor-space) connectivity estimators against VC artifact. In this paper, a criterion called Robustness Index (RI) is proposed to compare the robustness levels of connectivity estimators to channel couplings which are modeled by instantaneous linear mixing of quasi-independent components. Since the VC effects have instantaneous linear mixing nature, we expect RI to rank the connectivity estimators according to their robustness levels to VC artifact. RI is used to rank seven functional connectivity estimators: the absolute value of Pearson Correlation Coefficient (CC), Mutual Information (MI), Magnitude Squared Coherence (Coh), (1:1) Phase Locking Value ((1:1)PLV), the absolute value of Imaginary part of Coherency (ImC), Phase Lag Index (PLI) and Weighted Phase Lag Index (WPLI). The results for simulated data and a real EEG dataset show the connectivity estimators that are theoretically robust to VC artifact (ImC, PLI and WPLI) yield RI values near %100 and have the highest ranks, as expected. Also, for the simulated models in which the true VC effects and brain sources are known, ranking the connectivity estimators by RI is consistent with their robustness levels against VC artifact. Thissupports the possibility of using RI as a tool for ranking the robustness levels of connectivity estimators against VC artifact for real EEG/MEG datasets.

Tremor is one of the most frequent movement disorders which is involuntary and approximately sinusoidal. It affects various body joints such as elbow. Tremor on an elbow is considered as extension, flection, and rotation of the forearm. There are miscellaneous types of treatments for tremor one of which is electrical stimulation. In this research, we study existing stimulation methods in order to reduce tremor and control stimulation pulses. It should be notified that studying these methods requires 1- an electrical stimulator so that one can run natural experiments and estimate the validity of the model, 2- a skeletal- neuromuscular model in order to study the tremor dynamics and the system simulation, and 3- determining an appropriate stimulation scheme and control method in order that one can control the stimulation parameters to reduce tremor. The antagonist muscle stimulation technique for reducing tremor is in the form of either muscle co-contraction or anti-phase stimulation. In the former method, considering the fact that the time-dependent system has time-delay, disturbance, and non-linearities, a robust controller is needed. Hence, in this study, we take advantage of MPC controller because of its features. The results show that MPC controller is more satisfactory than the PID and fuzzy ones used in previous works and also demonstrate that one can theoretically reduce tremor by applying appropriate electrical stimulation.

Currently need for ultra low power wireless transmitters in medical applications are inevitable. In this paper a new transmitter for body-worn and implantable sensor nodes is presented. Most of the sensor nodes supply their power using energy harvesting instead of a battery, since the power earned by harvesting is limited, so the average and the peak power consumption of the sensor node must be minimized. Transmitter blocks which implemented in sensor nodes are too power consuming. So a new low power Binary Frequency Shift Keying (BFSK) transmitter based on sub-harmonic current mode injection locking, and edge combining technique has been proposed. The proposed transmitter was designed to make a mutual communication between sensor node and base station, so there is no need for complexity at receiver side. In order to reduce the consuming power at transmitter side, BFSK modulation is done at reference frequency to prevent usage of power consuming low phase noise oscillator at carrier frequency. A 34MHz reference clock is used and the frequency of reference clock multiplied by 12 for desired carrier frequency. The phase noise of the carrier at 1MHz frequency offset is -117 dBc/Hz. Total power consumption of the transmitter is about 144µW. The output carrier frequency is 408MHz. BFSK modulation scheme is used at the frequency much lower than the carrier frequency in order to reduce the power consumption.

Successful outcomes of Sparse Representation-based Classifier (SRC) and Sparse Subspace Clustering (SSC) in many applications motivated us to combine these methods and propose a hierarchical classifier. The main idea behind the SRC and SSC algorithms is to represent a data using a sparse linear combination of elementary signals so that those elementary signals which are similar to the data contribute mainly in the representation.In this paper, the performance of a sparse representation based classifier is improved by pre-clustering of training samples using the SSC algorithm. A two-stage SRC is then designed using the resulting clusters. A test data is classified by first determining the most similar cluster. The data label is subsequently found using the second stage classifier. The performance of the proposed method is evaluated considering cancer classification problem using the 14-Tumors microarray dataset. Due to low number of data samples per each class and high dimensionality of the data, this is a challenging problem.Curse of dimensionality,overfitting of theclassifier to the training data and computational complexity are the possible related problems. Our experimental results show that the proposed method outperforms some other state of the art classifiers.

Nowadays real time motion tracking have been receiving considerable attention in many applications and research fields such as rehabilitation, medicine and treatment. Recently MEMS accelerometers play an important role to attend desired result for these applications. This paper presents a new design for angle measurement device based on accelerometer sensor and Bluetooth module. Using Bluetooth module in addition to providing minimally obtrusive recording, allows you to connect to your personal computer and mobile quicker and easier. This system has made up of 2 complete 3 axis accelerometer ADXL330, which by giving sufficient data in 3D space allows us to investigate joint angle with DCMR method. The mentioned method in dynamic recording remarkably has less error in comparison to CMR method. As one application for this system, determination of elbow joint angle is studied. Eventually experimental recording of elbow joint angle in static and dynamic condition was done by applying CMR method. With reference to electrogoniometer output the maximum static and dynamic error were obtained respectively 3 and 6.1 degrees.

In this paper, a combined wavelet based mesh free method has been presented to solve the forward problem in magnetic induction tomography (MIT). Being a non-contact safe imaging technique,MIT has been an appropriate method for noninvasive industrial and medical imaging. In this imaging method, a primary magnetic field is applied by one or more excitation coils to induce eddy currents in the material to be studied, and then the secondary magnetic field from these eddy currents is detected in sensing coils.Image reconstruction is obtained from estimated electric conductivity coefficients by using measurement data and solutions of forward and inverse problems. In general, the forward problem is solved using finite element method (FEM) with acceptable accuracy but in problems involving moving objects or objects with changing geometrical appearance, mesh distortion is inevitable and susceptible to producing error in numerical results.Since the solution of the FEM depends on the mesh shape and boundary condition constraints are difficult to be applied to the mesh free method, in this paper, the combined wavelet based mesh free approach is suggested to resolve the disadvantages of both methods in the MIT forward problem. In order to apply interface conditions betweenthe two finite element and mesh free sub-domains, slope jump functions are entered to the set ofbasis functions.The simulation results obtained by the proposed method are compared with the FEM in terms of accuracy and computational cost.

Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is very complex and depends on radio frequency (RF) power and RF pulse shape. Two approaches have been used to saturate contrast agent (CA) protons: continuous wave CEST (CW-CEST) and pulsed CEST. To find the optimal RF pulse, numerical solution of Bloch-McConnell equations (BME) may be used. In this paperwe find the optimum values of RF pulse parameters that maximize the CEST contrast. Discrete pulses have lower specific absorption ratio (SAR) than CW RF pulses. However, since discretization is performed on continuous RF pulses, optimizing the continuous RF pulses leads to the optimization of discrete RF pulses. Therefore, in this paper, Rectangular, Gaussian and Fermi pulses are investigated as CW RF pulses. In this investigation, in addition to considering the SAR limitation, 60 dB approximation for the RF pulse amplitude is used. To compare the efficiency of pulses, their resultant flip angles (FA) are assumed equal. Efficiency of CW-CEST is investigated using two parameters, CEST ratio and SAR. According to these parametres, rectangular, Fermi and Gaussian RF pulses have the best performance respectively. Since implementation of rectangular RF is harder than Gaussian and Fermi RF pulses, Fermi and Gaussian RF pulses are desired. Our results suggest that it is possible to maximize CEST ratio by optimizing parameters of rectangular (with an amplitude of 5.7µT), Gaussian (σabout 0.7s) and Fermi (a-value about 0.3s) pulses. Results are verified by empirical formulation of CEST ratio.

FES based method used for rehabilitation of patients with spinal cord injury (SCI). One of these methods is FES cycling. FES cycling exercise has to be useful among SCI patients because of creating a periodic activity in the muscles of the lower extremities and stability of seating position. The major challenge for application of FES in rehabilitation is early fatigue occurrence in electrically stimulated muscles. Motor control system selects a low-cost path among the infinite possible route to the body's movements. High efficiency and the minimum rate of muscle fatigue are main characteristics of the motor control system. This type of control system is called muscle synergy. In this study, the quantification of muscle synergy between the core muscles in cycling has been done by non-negative matrix factorization (NMF) method and considering the kinesiology basis. Four synergies were determined as appropriate and optimal synergies to describe the cycling in different mechanical terms. VAF criteria with regard to the four synergies to describe cycling in speeds of 40, 50 and 60 rpm are 92±4, 92±3 and 91±4% respectively and torques, 5, 7 and 9 Nm are 91±3, 92±5 and 92±4% respectively. Correlation between Synergies extracted at different mechanical terms is 98.4 percent in average.