Full Research Paper
Implant / Implant's Designing & Manufacturing
Ehsan Mohammadi Mahmoei; Reza Lashgari; Behrouz Salamat
Volume 16, Issue 3 , December 2022, Pages 195-205
Abstract
The human body has five main senses of sight, hearing, taste, smell and touch. The defective performance of any of these senses causes us to solve this problem and use technology for this purpose. The sense of hearing is no exception and several attempts have been made to restore it, which has led to ...
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The human body has five main senses of sight, hearing, taste, smell and touch. The defective performance of any of these senses causes us to solve this problem and use technology for this purpose. The sense of hearing is no exception and several attempts have been made to restore it, which has led to the design of various implants. In this study, with the aim of investigating the function of the auditory midbrain implant (AMI) in restoring hearing ability, the cat’s auditory system has been stimulated in acoustic and electrical stimulation. Electrical stimuli are the result of AMI injecting current into the central nucleus of the inferior colliculus (ICC) and acoustic stimuli are the result of pure tone sound in the cat’s ear. After stimulation, responses were extracted from the primary auditory cortex of the cat's brain. Finally, a neural network (NN) with backpropagation-based modelling has been used. After data acquisition and processing, it was clear that AMI successfully stimulated the ICC. But it is associated with delays during stimulation. After model creation, it was found that the Levenberg-Marquardt algorithm with 10 neurons in the hidden layer had the best performance compared to the others with an error of 0.009. Also, both models show similar behaviour to frequency changes, but the electrical model at a constant frequency shows a bigger response at the output. Finally, the interval between the transmission of the neural message from the cochlear nucleus to the inferior colliculus was calculated at 9 milliseconds.
Full Research Paper
Gait Analysis
Ali Maleki; Elham Hasani
Volume 16, Issue 3 , December 2022, Pages 217-228
Abstract
Parkinson's disease is a neurodegenerative disease that causes severe movement disorders including bradykinesia, rigidity, and tremors. There is no cure for Parkinson's disease, only the symptoms can be managed. Parkinson's disease is diagnosed using the MDS-UPDRS global grading scale. In this scale, ...
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Parkinson's disease is a neurodegenerative disease that causes severe movement disorders including bradykinesia, rigidity, and tremors. There is no cure for Parkinson's disease, only the symptoms can be managed. Parkinson's disease is diagnosed using the MDS-UPDRS global grading scale. In this scale, four levels including slight, mild, moderate, and severe levels are defined for the disease. Recurrence plots and RQA features are tools for describing the behavior of chaotic systems and revealing hidden patterns in system dynamics. In this paper, the effect of Parkinson's disease progression on RQA chaotic features is studied. For this purpose, the dataset of the accelerometer mounted on the hand during the finger tapping test was used, which included 67 healthy data, 54 level one data, 66 level two data, 59 level three data, and 14 level four data. After pre-processing, the recurrence plots of the data were drawn and their RQA characteristics were calculated. Patterns of recurrence plots including separate recurrence points, diagonal lines, vertical lines, black squares, and horizontal and vertical white bands were investigated. According to the obtained results, the patterns of recurrence plots had significant differences among different levels of Parkinson's disease. Therefore, RQA features can be used to automatically determine the level of Parkinson's disease.
Full Research Paper
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Ali Maleki; Maedeh Azadimoghadam
Volume 16, Issue 3 , December 2022, Pages 229-240
Abstract
A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. ...
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A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. The first step to studying and ultimately reducing the destructive effects of fatigue is to identify the level of fatigue. Although frequency indices have been used for fatigue evaluation, the results of previous research in this field are inconsistent. Therefore, there is no detailed and comprehensive investigation of how fatigue affects frequency indices. In this paper, the evaluation of frequency-domain fatigue indicators has been done accurately and comprehensively. For this purpose, nine visual stimuli with different flickering frequencies were displayed to the subject, and they were asked to pay attention to the target cue. The visual stimulation was presented continuously, without rest to ensure that the fatigue occurs at the end of the test. Mean amplitude of theta, alpha, and beta bands, and 4-30Hz frequency band segments with 1Hz, 2Hz, and 4Hz steps were evaluated as fatigue indices. The results show that the mean amplitude of the frequency band of 8-9 Hz is more suitable for fatigue evaluation. This index has the most changes with fatigue in a state of wakeful relaxation of the subject and the mental effort to maintain the level of alertness in the fatigue state.
Full Research Paper
Human Computer Interaction / HCI
Hadi Soltanizadeh; Pouria Sharifi; Ali Maleki
Volume 16, Issue 3 , December 2022, Pages 241-255
Abstract
Losing of voice and larynx is a major problem for people with speech disorders. It creates serious and negative consequences on the quality of individual and group life of these people, especially in working environments. The development of an intelligent system based on electromyogram signals with the ...
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Losing of voice and larynx is a major problem for people with speech disorders. It creates serious and negative consequences on the quality of individual and group life of these people, especially in working environments. The development of an intelligent system based on electromyogram signals with the ability to recognize speech (without using sound) can be a window of hope for people who lost their larynx and voice due to cancer. Although progress and studies in this field are growing in our country and in different languages, but these studies have not been done for the Persian language. In this article, for the first time, recognition of Persian words was done using electromyogram of facial muscles. For this purpose, sEMG signals were collected from eight facial muscles and six volunteers while speaking twelve Persian words. Then, MFL, VAR, DAMV, LTKE, IQR and Cardinality features were extracted from each channel and each window from the signal, and the 432 features from each signal were reduced to 33 features using the PCA principal component analysis method. Finally, in order to recognize twelve Persian words, the features were given to SVM, KNN and RF classifiers. The average classification accuracy was 83.16%, 81.91% and 78.97%, respectively. Our evaluation in this article gives the hope that by using EMG signals it is possible to recognize the limited words of Persian language.
Full Research Paper
Bioelectrics
Zohre Mojiri; Amir Akhavan; Ehsan Rouhani
Volume 16, Issue 3 , December 2022, Pages 257-269
Abstract
Deep brain stimulation (DBS) is a technique to stimulate the deep areas of the brain which can be used in both invasive and non-invasive methods. In invasive DBS, the electrodes are surgically implanted inside the brain to achieve the desired depth of the stimulation. The invasive DBS approach suffers ...
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Deep brain stimulation (DBS) is a technique to stimulate the deep areas of the brain which can be used in both invasive and non-invasive methods. In invasive DBS, the electrodes are surgically implanted inside the brain to achieve the desired depth of the stimulation. The invasive DBS approach suffers from intracranial bleeding. One solution is using non-invasive DBS by temporal interference (TI) method. In TI stimulation, the constructive interference of two electric fields generated by two high-frequency sinusoidal currents increases the stimulation intensity at a certain depth. The objective of this paper is to investigate quantitatively as well as qualitative analysis of TI stimulation effect on the activation of primary motor cortex area of the rat. To this end, a 4-channel stimulator is used. The experiment is conducted on one anesthetized rat. The transcranial stimulation is applied by the electrode fixed on the skull with screw and the results are evaluated qualitatively and the quantitatively in the domains of time, frequency, and space. To quantify the results, a three-axis accelerometer sensor is used to record the movement acceleration of the right hand. The results showed that, the variation of the stimulation parameters (stimulation current intensity, frequency difference and ratio of currents of the two electrodes) changed the stimulation area inside the two hemispheres of the brain and movement range of the right hand. Moreover, the relationship between the difference frequency of the stimulation of the two pairs of electrodes and the range of motion was analyzed using a three-order polynomial regression model.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Parastoo Sadeghinia; Hamed Danandeh Hesar
Volume 16, Issue 3 , December 2022, Pages 271-287
Abstract
Phonocardiography (PCG) signals provide valuable information about the heart valves .These auditory signals can be useful in the early diagnosis of heart diseases. Automatic heart sound classification has a promising potential in the field of heart pathology. In this research, a new method based on machine ...
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Phonocardiography (PCG) signals provide valuable information about the heart valves .These auditory signals can be useful in the early diagnosis of heart diseases. Automatic heart sound classification has a promising potential in the field of heart pathology. In this research, a new method based on machine learning techniques is proposed for discriminating normal and abnormal heart sounds. In this method, first, the heart sounds are segmented into 4 main parts: S1, S2, systole and diastole segments. From these segments, statistical and time-frequency features are extracted for classification. Before classification, the distinctive features are selected using two approaches. In the first approach, the feature selection is accomplished using particle swarm optimization algorithm (PSO). In the second approach, we use Sequential Forward Feature Selection (SFFS) method. The proposed method was evaluated on the Physionet 2016 Challenge database using 10-fold cross-validation method. In this database, the number of normal and abnormal PCG signals are not balanced. Therefore, in this paper, the synthetic minority over-sampling technique (SMOTE) is applied to produce balanced data. The evaluation results showed that the proposed method can distinguish the normal heart sounds from abnormal ones with accuracy of 98/03% and sensitivity and specificity of 97.64%, 98.43%respectively.
Full Research Paper
Tissue Engineering
Sara Zadegan; Bahman Vahidi; Nooshin Haghighipour
Volume 16, Issue 3 , December 2022, Pages 289-299
Abstract
Repairing osteochondral defects (OCD) remains a formidable challenge due to the high complexity of native osteochondral tissue and the limited self-repair capability of cartilage. In this regard, the development of osteochondral tissue engineering with scaffolds seeded with stem cells along with mechanical ...
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Repairing osteochondral defects (OCD) remains a formidable challenge due to the high complexity of native osteochondral tissue and the limited self-repair capability of cartilage. In this regard, the development of osteochondral tissue engineering with scaffolds seeded with stem cells along with mechanical stimulation has been considered by the researchers as a new proposed technique for the repair of this tissue. In this study, at first we fabricated an integrated and biomimetic trilayered Silk Fibroin (SF) scaffold containing SF nano fibers in each layer. Then fluid wall shear stress in different areas of the scaffold was predicted in dynamic cell culture condition under the inlet velocity of 0.4 ml/min in a perfusion bioreactor using finite elements and fluid-structure interactions methods. Finally, using the simulation results, osteogenesis and chondrogenesis of rabbit adipose derived stem cells (RADSCs) were analyzed. The results showed that this novel osteochondral graft has a seamlessly integrated layer structure and a high degree of pore interconnectivity. The average size of the pores in the bone layer, middle layer, and cartilage were 76, 152, and 102 microns, respectively. In addition, this biomimetic scaffold presented compressive moduli of 0.4 MPa and uitimate tensile strength of 10 MPa in the wet state. Also, based on the simulation analyses, the shear stress distribution is more uniform if the bone layer is exposed to the fluid inlet path which facilitates bone differentiation. Good adhesion and infiltration of cells were observed after 14 days dynamic culture. The results of expression analysis of differentiated genes in bone and cartilage layer containing RADSc after 21 days of culture under static and dynamic conditions showed that perfusion flow significantly upregulated the expression of bone and cartilage genes in the respective layers and downregulated the hypertrophy gene expression in intermediate layer of scaffold.
