فصلنامه مهندسی پزشکی زیستی
سال دهم شماره 1 (بهار 1395)

The purpose of this article is to evaluate the efficiency of Canonical Correlation Analysis- Recursive Least Square (CCA-RLS)hybridmethod in ElectroOcluGram (EOG) artifact removal from ElectroEncephaloGram (EEG) signal and compare it with Independent Component Analysis (ICA), Canonical Correlation Analysis (CCA), Recursive Least Square (RLS)methods and ICA-RLS hybrid method. After decomposition of the noisy signal by CCA, the noisy components aredetected based ontheir kurtosis, and are filtered by RLS. As the result,the enhanced signal is reconstructed by mixing the original noise-free components and filtered components. In order to compare the methods quantitatively, two evaluation criteria, namely Mean Square Error (MSE) and Signal to Noise Ratio (SNR) are used.The MSE and SNR average values were calculated for five subject in four different channels. EEG data are taken from BCI2008. According to the results,the combination of CCA-RLS method has better performance compareto the other methods used in this paper.

Heart torsion is one of the biomechanical parameters that are sensitive to changes in both regional and global left ventricular (LV) function. In this study, angle of myocardium’s trajectory in three dimensions (Ф) was estimated by simultaneous use of two dimensional long apical and short axis views of LV septum sequential images. Then correlation of 3D angle and 2D rotation angle from long (χ) and short (θ) axis views respectively was estimated and compared at three levels of base, mid and apex of interventricular septum wall. Sequential two dimensional echocardiography images of long and short axis views with minimum temporal resolution 14 ms of 19 healthy men was recorded and analyzed. Interventricular septum wall motion at three levels of base, mid and apex were estimated using sequential images processing of echocardiography in long and short axis views with block matching algorithm throughout three cardiac cycles. Then correlation of 2D angle of rotation from long (χ) and short (θ) axis views was analyzed with three dimentional angular of myocardium’s trajectory (Ф) at three levels of base, mid and apex of interventricular septum wall. Ф, θ and χ angles at base level 16.33±3.01, 10.61±3.38 and 15.11±3.30 degrees, mid level 22.77±4.95, 7.78±2.96 and 16.72±2.66 degrees and apex level of interventricular septum wall 14.60±5.81, 10.37±5.48 and 8.79±3.32 degrees were extracted respectively. Regard to sensitivity of 3D angle to variation of motion in each of three dimensions, it is suggested for examination of biomechanical behavior myocardium in different pathologic conditions.

Knee implants still lacks sufficient design solutions to ensure improved long term performance without aseptic loosening and the subsequent revision surgery.The present paper, used full factorial design of experiment (DOE) method along with finite element analysis (FEA) to assess the influence of internal contours of femoral component on mechanical stability of the prosthesis, and the biomechanical stresses experienced by the femoral component, bone cement and the adjoining bone with preservation of the external contours.The WASPAS approach, as a multi criteria decision analysis (MCDA) technique, was then used to rank the alternative designs. The results of analysis of variance showed that the internal shape of femoral component contours influenced the performance measureswhere the angle between the distal and anterior cuts, the angle between the distal and posterior cuts, and the cement thickness were highlysignificant. The predictive mathematical models of each performance measureswre also estimated through statistical analysis. The ranking order and the following sensitivity analysis revealed that the top designs mostly had higher cement thickness and the original design was not the top choice for femoral component which by improving the current designbetter long term performance can be achieved.

In recent years, researchers have tried hardly to diagnose Parkinson's disease through finding its relation with the patient's speech signal. Also, many studies have been performed on determining the intensity of the disease and its relation with vocal impairment measures. In this paper, we aim to assess and compare the ability of extracting different feature sets from speech signal in order to Parkinson's disease diagnosis. Therefore, 132 features were used to measure vocal impairments from the voice signal of individuals vocalizing phoneme /a/. Then, we used RELIEF feature selection method and applied it to Support Vector Machine (SVM) classifier to choose the best feature of each class. A comparison was made between different feature sets, and finally discrimination percent 95.93 was reached to separate patients from the healthy ones using the combination of selected features. Results obtained from this research can be a very important step toward diagnosing Parkinson's disease non-invasively.

Positron emission tomography (PET) system is used in order to diagnose physiology changes in the body. Thus, the goal of the PET studies is to obtain a good quality and detailed image of organs by the PET scanner. The PET system performance and output image quality depend on the parameters including spatial resolution, scatter fraction, sensitivity, RMS contrast and SNR which the system was evaluated based on them. In this paper, system features and tomography method for the IRI-microPET system are considered, firstly. Then, image reconstruction algorithms (MLEM, SART, and FBP) were performed on sinogram and the performance and the acquired images quality were evaluated. The radial and tangential resolutions of 1.81 mm and 1.90 mm for 18F at the center of FOV were measured. The scatter fraction of 7.1% for the mouse phantom and the sensitivity of 1.74% in 4 ns timing window was measured. Finally, images quality was compared by RMS contrast and SNR factors, which MLEM algorithm has superiorityin comparison with the other reconstructed algorithms.The acquired results from IRI-MicroPET system were compared with available commercial animal PET scanner which the results show the good agreement between data.

The aim of this paper is to study brain effective connectivity based on directed transform function (DTF) using granger causality method. This connectivity was calculated for recorded data in different states of attention and consciousness, forming four different classes: attention-consciousness, attention-unconsciousness, inattention-consciousness, and inattention-unconsciousness. Some common indices were extracted and calculated from the connectivity matrices. Indices of these four classes were compared to see whether there is a significant difference among them or not. The Multivariate Autoregressive (MVAR) model was used to obtain the linear causal relations between channels. Furthermore, signals were divided into four frequency bands for more accurate investigation, and the existence of significant difference was investigated with two-way repeated measures test. Results indicated that and among twelve indices could show a significant difference (p

In this paper, a hybrid algorithm has been developed by analyzing the audio signals of the heart, that consists of extracting features based on chaos technique, reducing dimensions and analyzing the main components and classifying outputs by relying on comparative neuro-fuzzy networks. Uncertainty and high error in the diagnosis of inter-ventricular openings are one of the common problems with the previous methods. Due to the importance of the auto-diagnosis of this heart condition, it is necessary to be well-designed and far from error. Transmission of feature spaces to their mapping by the main component analysis algorithm is made by two steps, selecting the number of 18 to 25 attributes among about 50 extracted attributes that these informations are input of the class. The proposed classification classifies the adaptive fuzzy neural network system with the possibility of predicting the incidence of heart disease, which predicts the number of repetitions at the acceptable level of outputs by entering the data. The data are from the Umich database at the University of Michigan and include samples from the ventricular aperture. The ratio of data split in the learning and testing phase is from 0.9 to 0.1 (cross-check), and the K-fold validation method is used. Calculation of criteria such as accuracy, sensitivity and uncertainty by the concept of entropy in a hybrid algorithm suggests the proper performance of the proposed method.

Along with advancement in medical technologies, the academic field of Biomedical Engineering (BME) was developed. BME which was once considered as a subdivision of other disciplines, has gradually become an independent discipline with established departments. The extended medical and biological applications of the new discipline resulted in itsrapid progress. It is essential for academic centers to examine novel education and research areas of biomedical engineering every few years. In this paper we presented educational and research status of biomedical engineering among world's 50 top universities from different continents. We used three world university rankings (Time, QS, CWUR) to select top universities in 2016. Overally we studied 17 universities from America, 19 universities from Europe and 14 universities from Asia and Oceania. The undergraduate and postgraduate educational programs were presented and the independency status of biomedical engineering departments were studied using four models and results were compared among universities from different continents. The foundation year and number of academic staff of BME departmentswere further shown.Moreover, the BME researchfiledswere shown and compared among top universities from different contients and the most prevalent research areas were presented.