Full Research Paper
Bioceramics / Bioglasses
Mehrdad Davoudi; S. Mohammad Reza Shokouhyan; Mahdi Bagheri Rouchi; Masoud Abdollahi; Soha Bervis; Maryam Hoviat Talab; Mohamad Parnianpour
Volume 14, Issue 2 , July 2020, Pages 81-96
Abstract
An open research question is how the central nervous system (CNS) to find a solution for the problem of redundancy or degree of freedom in the human shoulder motion control. We used time-varying synergy theory in which assumed that the relative activation between muscles is time-varying, to investigate ...
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An open research question is how the central nervous system (CNS) to find a solution for the problem of redundancy or degree of freedom in the human shoulder motion control. We used time-varying synergy theory in which assumed that the relative activation between muscles is time-varying, to investigate the combination of activation patterns of muscles in twelve 3-D hand-held exercises by Flexi-Bar using Anybody Technology software (A/S, Aalborg, Denmark). Using activation of 12 muscles and the moment across the joint as an input matrix for the optimization procedure to extract functional time-varying synergies, time delays and amplitude coefficients, the achieved 5 tonic and phasic synergies explained 79% of the data variation. Matching pursuit procedure and non-negative least square used to find timing shifts and amplitude coefficients respectively. Considering a new exercise out of the primary database, 60% of the activation patterns reconstructed using time-varying synergies. Although the extracted synergies seem to be directionally tuned, the results show that due to the same velocity in all exercises and also because the torque which that was applied due to the weight of the bar and arm on the joint is not significant, both timing shifts and separation phasic-tonic parts of the activation patterns provide no further explanation on CNS behavior and finding them causes unnecessary computational cost. Future study can focus on the comparison of synergies between two or more groups of exercises by the Flexi-Bar such as holding the bar vertically or horizontally with swinging it up and down or back and forth.
Full Research Paper
Speech processing
Mohammad Bahador Najafi; Mansour Vali
Volume 14, Issue 2 , July 2020, Pages 97-107
Abstract
After Alzheimer, Parkinson's disease is known as the most common malignant disease of the nervous system. One of the common obstacles of this disease is the expansion of speech disorders. Since the speech production in humans is made by combination of vibration of the vocal cords (phonatory section) ...
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After Alzheimer, Parkinson's disease is known as the most common malignant disease of the nervous system. One of the common obstacles of this disease is the expansion of speech disorders. Since the speech production in humans is made by combination of vibration of the vocal cords (phonatory section) and then passage through the resonator in vocal tract (articulatory section), it is expected that both of these sections to be impaired. In this study, by using a noninvasive method, it is intended to diagnose Parkinson's disease from speech signal of each subject; for this purpose, using 3 sustain vowels in Persian language recorded from 48 people (27 people with Parkinson's disease and 21 healthy people), it has been evaluated to assess the extent of damage to both phonatory and articulatory sections. The phonatory model can include features such as jitter, shimmer, fundamental frequencies, opening and closing cycling time of the glottal pulses. On the other hand, for the articulatory section, features such as first, second, and third formmants, zero crossing rates, MFFCs, and LPC are investigated. In this study, 38 feature categories were extracted and four statistical parameters of mean, standard deviation, skewness and kurtosis were calculated. Genetic Algorithm was used to identify the optimum features. Then, using the SVM, KNN and the Decision Tree classifiers, the optimum extracted features are classified to determine whether a person is patient or healthy. Finally for the main aim of this study, the results of both phonatory and articulatory sections were compared and challenged. The results of this study showed that phonatory features with accuracy of 96.1±1.2% were more useful than articulatory section in diagnosing of Parkinson. Also it was proved that vowel /u/ has more significant role in the diagnosis of Parkinson's disease compared to other vowels by accuracy of 97.6%.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Maryam Dorvashi; Neda Behzadfar; Ghazanfar Shahgholian
Volume 14, Issue 2 , July 2020, Pages 109-119
Abstract
Consumption of alcohol contributes to disorders in brain. In this study, in order to detect the consumption of alcohol, electroencephalogram (EEG) signal of 20 participants (10 alcoholic and 10 control subjects) recorded by 64 channels was investigated. Frequency and non-frequency features of EEG signal ...
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Consumption of alcohol contributes to disorders in brain. In this study, in order to detect the consumption of alcohol, electroencephalogram (EEG) signal of 20 participants (10 alcoholic and 10 control subjects) recorded by 64 channels was investigated. Frequency and non-frequency features of EEG signal including power spectrum of signal, permutation entropy, approximate entropy, Katz fractal dimension and Petrosion fractal dimension were extracted to analyses the EEG signal. Statistical analysis was used to investigate the significant differences between the alcohol and control groups. The Davis-Bouldin (DB) criterion was used to select the best channel distinguishing between the alcoholic and non-alcoholic EEG signal. Results showed that between frequency features, power of lower2 alpha frequency decreased in alcoholic individuals and regarding the DB criterion, the CP3 channel (DB=1.7638) showed the best discrimination between the alcohol and control groups. Also, among the non-frequency features, the Katz fractal dimension increased in the control group and FP2 channel (DB = 0.862) had the best discrimination. Eventually, power of Lower2-alpha frequency band and Katz fractal dimension fed into the nearest neighbor classifier (KNN), 71% and 93% accuracy were achieved, respectively. According to the results, it can be concluded that the best feature and channel discriminating between alcohol and control groups is the Katz fractal dimension and FP2 channel.
Full Research Paper
Biomechanics of Bone / Bone Biomechanics
Fereshteh Alizadeh Fard; Majid Mirzaei
Volume 14, Issue 2 , July 2020, Pages 121-131
Abstract
Regarding the application of testing and analysis of bone fractures in both medical and engineering fields, finding proper specimens for measuring fracture properties is important. In this study, the experimental and numerical fracture analyses of bovine cortical bone were performed for 4 anatomical ...
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Regarding the application of testing and analysis of bone fractures in both medical and engineering fields, finding proper specimens for measuring fracture properties is important. In this study, the experimental and numerical fracture analyses of bovine cortical bone were performed for 4 anatomical regions using arc-shaped specimens. The tensile fracture tests for arc-shaped specimens were performed at ambient temperature. In practice, the stress intensity factor was calculated using standard analytical formula for arc-shaped specimens and also the related finite element (FE) models. In order to validate the FE models, the stress and strain analyses results were compared with the results obtained from digital image correlation (DIC) method. The very good agreement between these results was indicative of the accuracy of FE analyses. There were also good correlations between the initiation and propagation of crack from both experimental and FE results and the measured fracture toughness values were in good agreement with those reported in the literature. The results of this study showed that the analytical stress intensity expressions can give accurate results for the arc-shaped specimens excised from posterior and anterior regions. However, for the medial and lateral regions only the FE models can provide the required accuracy.
Full Research Paper
Biomechanics / Biomechanical Engineering
Mostafa Haj Lotfalian; Mohammad Hadi Honarvar
Volume 14, Issue 2 , July 2020, Pages 133-142
Abstract
Margin of stability is a method to assess the dynamic stability in the clinic and laboratory, which is influenced by position and linear velocity of the center of mass (CoM). In this study, the stability factor was calculated by the margin of stability (MoS) method and was used as a cost function to ...
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Margin of stability is a method to assess the dynamic stability in the clinic and laboratory, which is influenced by position and linear velocity of the center of mass (CoM). In this study, the stability factor was calculated by the margin of stability (MoS) method and was used as a cost function to plan movement trajectory of sit to stand. 10 healthy young men were selected in this study and their sit to stand movement were filmed by Optitrack motion capture system. A two-dimensional and four-segment model was defined based on the governing equations of motion to calculate position of CoM, joints torque and using that in optimization process. After calculating the subject’s stability factor by MoS method, the time integral of MoS (C1), the maximum and minimum of MoS (C2) and the time integral of the square of MoS (C3) were defined as the cost functions. genetic algorithm was used to find the optimal model. To determine the quality of predicted trajectories and compare it with the subject’s pattern, root mean square error (RMSE) was used. According to the results of this study, a model which was optimized by C3, predicted the movement trajectory of subjects with 19 and 40 percent less error than C1 and C2 respectively.Nevertheless, none of the models could correctly reconstruct the subjects’ movement trajectory. In a nutshell, using MoS exclusively as a cost function, is not a good choice to predict and plane the trajectory of whole-body movements.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Alireza Talesh Jafadideh; Babak Mohammadzadeh Asl
Volume 14, Issue 2 , July 2020, Pages 143-157
Abstract
Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors. Comparison study between ASD and typically control (TC) subjects through magnetic resonance imaging (MRI) provides valuable understanding ...
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Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors. Comparison study between ASD and typically control (TC) subjects through magnetic resonance imaging (MRI) provides valuable understanding for differences in brain function. Recently, through dynamic functional connectivity (DFC) analysis, it is found that brain functional connectivity possesses dynamic nature and shows transient connectivity patterns (“states”) repeating over time. In this comparison study between ASD and TC, we employed the rest functional MRI (rfMRI) data of San Diego State University (SDSU) of ABIDE II database to examine the brain intra and inter network connectivity and also to investigate the relations of age and social responsiveness scale (SRS) score (score measuring autistic traits) to brain inter regions connectivity strength. These aims were implemented in all DFC states. The ASD subjects experienced more the state with less intra and inter network connections. Further, the DMN segregation reduction from other functional networks emerged as a common them. Furthermore, in ASD, the connection strength between auditory and visual networks was decreased by increasing the age. In ASD, the SRS had more positive relation to connectivity strength existing between cerebellar, auditory, visual networks and cognitive control network in comparison to TC. All these results demonstrate that some differences exist in brain network connection of ASD in comparison to the TC subjects and these differences can be more distinctively revealed by employing DFC analysis.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Nasrin Sho'ouri
Volume 14, Issue 2 , July 2020, Pages 159-168
Abstract
Previous research has shown that eye movements in people with Attention Deficit Hyperactivity Disorder (ADHD) and healthy people were different, and it is possible that there is a difference between the two groups' EOG signals. Therefore, in the present study, the recorded EOG signals of 30 children ...
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Previous research has shown that eye movements in people with Attention Deficit Hyperactivity Disorder (ADHD) and healthy people were different, and it is possible that there is a difference between the two groups' EOG signals. Therefore, in the present study, the recorded EOG signals of 30 children with ADHD and 30 healthy children were examined during performing an attentional related task. For this purpose, the scaling exponents of the two groups' EOG signals were calculated and the differences between the two groups were examined using statistical tests. The EOG signals were then classified using a Growing Neural Gas network. The results show that the scaling exponents of the EOG signals in children with ADHD were significantly higher than that of healthy children (p < 0.001). This result shows that the decay slope of power spectrum in ADHD children is more as compared to healthy children. In addition, the EOG signals were classified into two groups with a detection accuracy of 72.22±2.8%. The results of this study could be used to design a course of treatment with EOG biofeedback to treat or reduce the symptoms of people with ADHD.
