Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043923
A. Saeedi, F. Almasganj, Malike Pourebrahim
Ability to walk plays an important role in human daily activities. During walking, the pressure pattern of the sole of the foot contains valuable information about the anatomic and physiologic situation of the body. Plantar pressure measurement systems can provide this information for scientists. In this paper, a device developed to measure plantar pressure and wirelessly transmit the data to a host computer is introduced in details. The pressure sensors are placed in a normal shoe. 32 force sensitive resistors (FSRs) are arranged in different areas of the insole of the shoe. An ARM-based microcontroller converts the plantar pressure to a digital pattern. This pattern is then transmitted to a host laptop, by means of a RF module. This device can be used outdoors and the shoe-wearing subjects can easily walk up to 60 meter far from the laptop. A graphical user interface (GUI) is designed to manage displaying and saving process of the received data and also to role in calibrating sequence of the FSRs. The system was tested by 5 volunteers and its abilities were successfully verified.
{"title":"Plantar pressure monitoring by developing a real-time wireless system","authors":"A. Saeedi, F. Almasganj, Malike Pourebrahim","doi":"10.1109/ICBME.2014.7043923","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043923","url":null,"abstract":"Ability to walk plays an important role in human daily activities. During walking, the pressure pattern of the sole of the foot contains valuable information about the anatomic and physiologic situation of the body. Plantar pressure measurement systems can provide this information for scientists. In this paper, a device developed to measure plantar pressure and wirelessly transmit the data to a host computer is introduced in details. The pressure sensors are placed in a normal shoe. 32 force sensitive resistors (FSRs) are arranged in different areas of the insole of the shoe. An ARM-based microcontroller converts the plantar pressure to a digital pattern. This pattern is then transmitted to a host laptop, by means of a RF module. This device can be used outdoors and the shoe-wearing subjects can easily walk up to 60 meter far from the laptop. A graphical user interface (GUI) is designed to manage displaying and saving process of the received data and also to role in calibrating sequence of the FSRs. The system was tested by 5 volunteers and its abilities were successfully verified.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130476558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043930
H. Yousefi, M. Fatehi, Mohsen Bahrami, R. Zoroofi
Extracting the structures of interest accurately is one of the main challenges in medical imaging segmentation. Statistical models of shape are a promising approach for robust and automatic segmentation of medical image data. This work describes the construction of a statistical shape model of the Radius bone. For 3-D model-based approaches, however, building the 3-D shape model from a training data set of segmented instances of an object is a major challenge and currently remains an open problem. In this study, we propose an active contour image segmentation method for three-dimensional (3-D) medical images. Our dataset contains T1-weighted images of hand wrist in coronal view. Such images are usually acquired in 9 slices, but we also used 27 slices images in which the spatial resolution is improved by reducing the in depth from 3mm to 1mm. In this study we use 27-slices MRI images to segment radius bone due to their higher resolutions in comparison to 9-slices images. First, using 2D active contour algorithm, radius bone is segmented in coronal slices automatically. Then, a statistical model of radius bone is derived and its mean model is used as the initial mask for 3D active contour algorithm, and 9-slices images are segmented using this algorithm. To compare the 2D and 3D active contour algorithms, 27-slices images are segmented through produced statistical atlas of mean model. Comparison of obtained segmentation and manual segmentation shows that segmentation accuracy in 9-slices images which use mean model will be increased from 75.68% to 91.57%. Acquisition of 9-slicese images takes a shorter time (1/3) in comparison to 27-slices images; therefore, we also derived the statistical model of 9-slices images. In the future works we utilize the proposed approach as part of a computer-aided diagnosis system for bone age estimation.
{"title":"3D statistical shape models of radius bone for segmentation in multi resolution MRI data sets","authors":"H. Yousefi, M. Fatehi, Mohsen Bahrami, R. Zoroofi","doi":"10.1109/ICBME.2014.7043930","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043930","url":null,"abstract":"Extracting the structures of interest accurately is one of the main challenges in medical imaging segmentation. Statistical models of shape are a promising approach for robust and automatic segmentation of medical image data. This work describes the construction of a statistical shape model of the Radius bone. For 3-D model-based approaches, however, building the 3-D shape model from a training data set of segmented instances of an object is a major challenge and currently remains an open problem. In this study, we propose an active contour image segmentation method for three-dimensional (3-D) medical images. Our dataset contains T1-weighted images of hand wrist in coronal view. Such images are usually acquired in 9 slices, but we also used 27 slices images in which the spatial resolution is improved by reducing the in depth from 3mm to 1mm. In this study we use 27-slices MRI images to segment radius bone due to their higher resolutions in comparison to 9-slices images. First, using 2D active contour algorithm, radius bone is segmented in coronal slices automatically. Then, a statistical model of radius bone is derived and its mean model is used as the initial mask for 3D active contour algorithm, and 9-slices images are segmented using this algorithm. To compare the 2D and 3D active contour algorithms, 27-slices images are segmented through produced statistical atlas of mean model. Comparison of obtained segmentation and manual segmentation shows that segmentation accuracy in 9-slices images which use mean model will be increased from 75.68% to 91.57%. Acquisition of 9-slicese images takes a shorter time (1/3) in comparison to 27-slices images; therefore, we also derived the statistical model of 9-slices images. In the future works we utilize the proposed approach as part of a computer-aided diagnosis system for bone age estimation.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130480166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043912
R. Shalbaf, A. Mehrnam, H. Behnam
Depth of anesthesia estimation with the Electroencephalogram (EEG) is a main current challenge in anesthesia studies. This paper proposes an original method founded on combination of permutation entropy and frequency measure to calculate an index, called Brain function index (BFI), to quantify depth of anesthesia. As EEG derived features characterize different aspects of EEG signal, it would be logical to utilize multiple features to evaluate the effect of anesthetic. Such a method implemented in the Saadat brain function assessment module (Saadat Co., Tehran, Iran). The BFI and commercial RE index as employed in the Datex-Ohmeda monitor are applied to EEG signals gathered from 18 patients during sevoflurane anesthesia. The results show that both BFI and RE indices track the changes in EEG especially at deep anesthesia state. However, the BFI index makes better response about the point of loss of consciousness and it can be derived with significantly less computational complexity. Taking into account the high accuracy of this method, an innovative EEG processing device may be extended to help the anesthetists to estimate the depth of anesthesia precisely.
{"title":"Depth of anesthesia indicator using combination of complexity and frequency measures","authors":"R. Shalbaf, A. Mehrnam, H. Behnam","doi":"10.1109/ICBME.2014.7043912","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043912","url":null,"abstract":"Depth of anesthesia estimation with the Electroencephalogram (EEG) is a main current challenge in anesthesia studies. This paper proposes an original method founded on combination of permutation entropy and frequency measure to calculate an index, called Brain function index (BFI), to quantify depth of anesthesia. As EEG derived features characterize different aspects of EEG signal, it would be logical to utilize multiple features to evaluate the effect of anesthetic. Such a method implemented in the Saadat brain function assessment module (Saadat Co., Tehran, Iran). The BFI and commercial RE index as employed in the Datex-Ohmeda monitor are applied to EEG signals gathered from 18 patients during sevoflurane anesthesia. The results show that both BFI and RE indices track the changes in EEG especially at deep anesthesia state. However, the BFI index makes better response about the point of loss of consciousness and it can be derived with significantly less computational complexity. Taking into account the high accuracy of this method, an innovative EEG processing device may be extended to help the anesthetists to estimate the depth of anesthesia precisely.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043910
Marzieh Mohammadi, S. H. Sardouie, M. Shamsollahi
Epilepsy is a brain disorder that 1% of people population are suffering from. One of the proper non-invasive equipment for diagnosis and analysis of this disease is electroencephalogram (EEG) recordings. However, EEG signals are often contaminated with noises and artifacts that hide epileptic signals of interest. Independent Component Analysis (ICA) is a common Blind Source Separation (BSS) method to denoise EEG signals. ICA has been proved as a worthwhile method to separate the signals of interest from noise and artifacts; nevertheless, it also has some weaknesses. In this work, to improve ICA performance in denoising context, we present an algorithm based on combination of ICA and Time Varying AutoRegressive (TVAR) model for denoising of interictal EEG signals. TVAR model is used serially after ICA method for interictal spike enhancement. The coefficients of TVAR model are estimated using Kaiman filter. The results indicate the proposed algorithm is better than ICA in terms of performance for very low Signal-to-Noise Ratio (SNR) values.
{"title":"Denoising of interictal EEG signals using ICA and Time Varying AR modeling","authors":"Marzieh Mohammadi, S. H. Sardouie, M. Shamsollahi","doi":"10.1109/ICBME.2014.7043910","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043910","url":null,"abstract":"Epilepsy is a brain disorder that 1% of people population are suffering from. One of the proper non-invasive equipment for diagnosis and analysis of this disease is electroencephalogram (EEG) recordings. However, EEG signals are often contaminated with noises and artifacts that hide epileptic signals of interest. Independent Component Analysis (ICA) is a common Blind Source Separation (BSS) method to denoise EEG signals. ICA has been proved as a worthwhile method to separate the signals of interest from noise and artifacts; nevertheless, it also has some weaknesses. In this work, to improve ICA performance in denoising context, we present an algorithm based on combination of ICA and Time Varying AutoRegressive (TVAR) model for denoising of interictal EEG signals. TVAR model is used serially after ICA method for interictal spike enhancement. The coefficients of TVAR model are estimated using Kaiman filter. The results indicate the proposed algorithm is better than ICA in terms of performance for very low Signal-to-Noise Ratio (SNR) values.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128424840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043947
Armita Faghani Jadidi, R. Davoodi, M. Moradi, Ali Yoonessi
Attention is amongst the high level cognitive task which is associated with complex process in the brain. But in almost all researches, this process is associated with a secondary task, (like numeration). Some believe that the link between these two cognitive activities (i.e. attention and numeration) in human brain is very close and intricate. However, the goal of this research is to evaluate the side effects of counting on visual attention in order to clarify the definition of Attention through brain signals. In previous related researches, only the qualitative impacts of counting have been dealt with and endeavor in this field is a new effort. We used a novel psychophysics task to explore the impact of this extra task on visual top-down attention. EEG was recorded during the task from 48 subjects in occipital, Parietal and frontal lobes. Target-locked ERPs for attention with and without numerating were constructed. Time features corresponding to P300 component were extracted for all eight channels separately. Common feature selection method and classifiers were employed to separate two classes (attention with numeration and pure attention). The results indicate that our task was capable of separation and some of the predefined ERP, time features are meaningful while attention is with numeration. As a result, we have introduced ERP features which belong to this separation and also determined the most relevant brain areas. To our knowledge, this is the first time that this quantitative separation is performed.
{"title":"The impact of numeration on visual attention during a psychophysical task; An ERP study","authors":"Armita Faghani Jadidi, R. Davoodi, M. Moradi, Ali Yoonessi","doi":"10.1109/ICBME.2014.7043947","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043947","url":null,"abstract":"Attention is amongst the high level cognitive task which is associated with complex process in the brain. But in almost all researches, this process is associated with a secondary task, (like numeration). Some believe that the link between these two cognitive activities (i.e. attention and numeration) in human brain is very close and intricate. However, the goal of this research is to evaluate the side effects of counting on visual attention in order to clarify the definition of Attention through brain signals. In previous related researches, only the qualitative impacts of counting have been dealt with and endeavor in this field is a new effort. We used a novel psychophysics task to explore the impact of this extra task on visual top-down attention. EEG was recorded during the task from 48 subjects in occipital, Parietal and frontal lobes. Target-locked ERPs for attention with and without numerating were constructed. Time features corresponding to P300 component were extracted for all eight channels separately. Common feature selection method and classifiers were employed to separate two classes (attention with numeration and pure attention). The results indicate that our task was capable of separation and some of the predefined ERP, time features are meaningful while attention is with numeration. As a result, we have introduced ERP features which belong to this separation and also determined the most relevant brain areas. To our knowledge, this is the first time that this quantitative separation is performed.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130611838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043898
S. Nazari, M. Amiri, K. Faez, E. Karami
Pathophysiologic neural synchronization is a sign of several neurological disorders such as parkinson and epilepsy. In addition, based on established neurophysiologic findings, astrocytes (more type of glial cells) regulate dynamically the synaptic transmission and have key roles in stabilizing neural synchronization. Therefore, in the present study, a new model for digital astrocyte-inspired stimulator is proposed and constructed to break the synchronous oscillations of a minimal network. The minimal network is composed of two Hopf oscillators connected via gap-junction. The complete digital circuit of the closed loop system that is the proposed astrocyte-inspired stimulator and the coupled Hopf oscillators are implemented in hardware on the ZedBoard development kit. The results of MATLAB, ModelSim simulations and FPGA implementations confirm that the digital proposed astrocyte-inspired stimulator can effectively desynchronize the synchronous oscillations of the coupled Hopf oscillator with a demand-controlled characteristic. In this way, the designed digital stimulator not only does not suppress oscillator natural features but also it successfully maintains the desired asynchronous activity.
{"title":"A novel digital circuit for astrocyte-inspired stimulator to desynchronize two coupled oscillators","authors":"S. Nazari, M. Amiri, K. Faez, E. Karami","doi":"10.1109/ICBME.2014.7043898","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043898","url":null,"abstract":"Pathophysiologic neural synchronization is a sign of several neurological disorders such as parkinson and epilepsy. In addition, based on established neurophysiologic findings, astrocytes (more type of glial cells) regulate dynamically the synaptic transmission and have key roles in stabilizing neural synchronization. Therefore, in the present study, a new model for digital astrocyte-inspired stimulator is proposed and constructed to break the synchronous oscillations of a minimal network. The minimal network is composed of two Hopf oscillators connected via gap-junction. The complete digital circuit of the closed loop system that is the proposed astrocyte-inspired stimulator and the coupled Hopf oscillators are implemented in hardware on the ZedBoard development kit. The results of MATLAB, ModelSim simulations and FPGA implementations confirm that the digital proposed astrocyte-inspired stimulator can effectively desynchronize the synchronous oscillations of the coupled Hopf oscillator with a demand-controlled characteristic. In this way, the designed digital stimulator not only does not suppress oscillator natural features but also it successfully maintains the desired asynchronous activity.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127545133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043888
A. Jalali, M. Ayani
Thermal dose indicates the level of thermal lesion. During a thermal therapy, desired thermal dose distribution along a tissue can be obtained by proper manipulation of heat source. In this study, the inverse heat transfer problem is applied to estimate the external heat source in a living tissue for a desired thermal dose. The inverse method is the conjugate gradient method with the adjoint problem. The inverse method is function estimation type, since there is not any information exists about the distribution form of the external heat source. The results show that the heat source is estimated precisely for the test cases discussed in this paper.
{"title":"Thermal dose optimization in a living tissue with conjugate gradient method","authors":"A. Jalali, M. Ayani","doi":"10.1109/ICBME.2014.7043888","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043888","url":null,"abstract":"Thermal dose indicates the level of thermal lesion. During a thermal therapy, desired thermal dose distribution along a tissue can be obtained by proper manipulation of heat source. In this study, the inverse heat transfer problem is applied to estimate the external heat source in a living tissue for a desired thermal dose. The inverse method is the conjugate gradient method with the adjoint problem. The inverse method is function estimation type, since there is not any information exists about the distribution form of the external heat source. The results show that the heat source is estimated precisely for the test cases discussed in this paper.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115004220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043904
Ali Marjaninejad, F. Almasganj, Ata Jodeiri Sheikhzadeh
Empirical Mode Decomposition (EMD) is a very powerful, signal dependent algorithm which decomposes signals as a set of Intrinsic Mode Functions (IMFs). The focus of this paper is on improving Signal to Noise Ratio (SNR) of noise contaminated Electrocardiogram (ECG) signal by applying a modified version of the Ensemble Empirical Mode Decomposition (EEMD) method. This method is utilized on synchronized sequential ECG beats of an ECG record. Since this method has a reasonable computational complexity and operates on the recorded signals, it can also be used in online applications. In this study, the achieved results with SNR of 12 for the EMD have been reported as 4.37×10-4 in terms of Mean Square Error (MSE) and the MSE for the proposed EEMD for the same records have been reported as low as 1.08×10-4. The experiments and results provided in this study have shown very promising performances compare to other methods such as simple EMD. In this paper, after confirming the fact that the intrinsic white noise is generally allocated to the first two IMFs of a contaminated ECG signal, it has been reported that the best results for the proposed EEMD method, in terms of MSE, have been achieved by removing the first two IMFs of the synchronized sequential beats of the input signals. Finally, the optimality and the efficiency of the proposed method have been evaluated in this paper by a comparison with two other methods, namely the EMD of the average signal and the simple averaging method.
{"title":"Online signal to noise ratio improvement of ECG signal based on EEMD of synchronized ECG beats","authors":"Ali Marjaninejad, F. Almasganj, Ata Jodeiri Sheikhzadeh","doi":"10.1109/ICBME.2014.7043904","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043904","url":null,"abstract":"Empirical Mode Decomposition (EMD) is a very powerful, signal dependent algorithm which decomposes signals as a set of Intrinsic Mode Functions (IMFs). The focus of this paper is on improving Signal to Noise Ratio (SNR) of noise contaminated Electrocardiogram (ECG) signal by applying a modified version of the Ensemble Empirical Mode Decomposition (EEMD) method. This method is utilized on synchronized sequential ECG beats of an ECG record. Since this method has a reasonable computational complexity and operates on the recorded signals, it can also be used in online applications. In this study, the achieved results with SNR of 12 for the EMD have been reported as 4.37×10-4 in terms of Mean Square Error (MSE) and the MSE for the proposed EEMD for the same records have been reported as low as 1.08×10-4. The experiments and results provided in this study have shown very promising performances compare to other methods such as simple EMD. In this paper, after confirming the fact that the intrinsic white noise is generally allocated to the first two IMFs of a contaminated ECG signal, it has been reported that the best results for the proposed EEMD method, in terms of MSE, have been achieved by removing the first two IMFs of the synchronized sequential beats of the input signals. Finally, the optimality and the efficiency of the proposed method have been evaluated in this paper by a comparison with two other methods, namely the EMD of the average signal and the simple averaging method.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123509242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043893
Zakieh Alihemmati, B. Vahidi, N. Haghighipour
Mesenchymal stem cells (MSCs) have the potential to differentiate to other cells and this feature makes it as an attractive source in tissue engineering. Mesenchymal stem cells are subjected to many mechanical stimuli in vivo such as an in vivo pressure loading and one of the cell respond to these stimuli is differentiation to cartilage or bone cell. One of the most significant ways for this goal is a process which is called mechanotransduction which can activate many biochemical signals in cell components. The biomechanical pathways for signal transmission are unknown and recent developmental findings indicate that forces which cause cell deformation are involved. Cell behavior is analyzed in this paper based on mechanical behavior of cell components. This study introduces an ideal method using finite element modeling which focuses on the cell mechanics using computational tools. This study is conducted in order to simulate an experimental situation in which cell deformation occurs. This method is used to analyze the strain distribution in the cell and play a key role in estimating the cell behavior under pressure loading.
{"title":"Mechanical modulation study of an adipose-derived mesenchymal stem cell under pressure loading: A numerical investigation on cell engineering","authors":"Zakieh Alihemmati, B. Vahidi, N. Haghighipour","doi":"10.1109/ICBME.2014.7043893","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043893","url":null,"abstract":"Mesenchymal stem cells (MSCs) have the potential to differentiate to other cells and this feature makes it as an attractive source in tissue engineering. Mesenchymal stem cells are subjected to many mechanical stimuli in vivo such as an in vivo pressure loading and one of the cell respond to these stimuli is differentiation to cartilage or bone cell. One of the most significant ways for this goal is a process which is called mechanotransduction which can activate many biochemical signals in cell components. The biomechanical pathways for signal transmission are unknown and recent developmental findings indicate that forces which cause cell deformation are involved. Cell behavior is analyzed in this paper based on mechanical behavior of cell components. This study introduces an ideal method using finite element modeling which focuses on the cell mechanics using computational tools. This study is conducted in order to simulate an experimental situation in which cell deformation occurs. This method is used to analyze the strain distribution in the cell and play a key role in estimating the cell behavior under pressure loading.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124487465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043902
Razieh Falahian, M. M. Dastjerdi, S. Gharibzadeh
The precipitous advancements in the field of modeling of dynamical systems, which are the result of numerous relevant investigations, are the evidence of its fundamental importance. Not only does the modeling of the behavior of dynamical systems such as biological systems play an important role in the accurate perception and analysis of these systems, but it also becomes feasible to perfectly predict and control their behaviors. The results of the majority of these researches have indicated that chaotic behavior is a prevalent feature of complex interactive systems. Our achieved results indicate that artificial neural networks provide us the most efficacious means to model the underlying dynamics of these systems. In this paper, we represent the results of utilizing a specific neural network to model some famous chaotic systems such as Lorenz. The main aspect of our technique is training the neural network with a chaotic map. With this aim, at first, bifurcation diagram of the points obtained by applying Poincaré section on the time series is plotted. The specified neural network is then trained with the extracted map. We conclude the paper by evaluating the accuracy and robustness of our model. The capability of the selected neural network to model the complex behavior of dynamical systems is indeed verified, even at the presence of noise.
{"title":"Pragmatic modeling of chaotic dynamical systems through artificial neural network","authors":"Razieh Falahian, M. M. Dastjerdi, S. Gharibzadeh","doi":"10.1109/ICBME.2014.7043902","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043902","url":null,"abstract":"The precipitous advancements in the field of modeling of dynamical systems, which are the result of numerous relevant investigations, are the evidence of its fundamental importance. Not only does the modeling of the behavior of dynamical systems such as biological systems play an important role in the accurate perception and analysis of these systems, but it also becomes feasible to perfectly predict and control their behaviors. The results of the majority of these researches have indicated that chaotic behavior is a prevalent feature of complex interactive systems. Our achieved results indicate that artificial neural networks provide us the most efficacious means to model the underlying dynamics of these systems. In this paper, we represent the results of utilizing a specific neural network to model some famous chaotic systems such as Lorenz. The main aspect of our technique is training the neural network with a chaotic map. With this aim, at first, bifurcation diagram of the points obtained by applying Poincaré section on the time series is plotted. The specified neural network is then trained with the extracted map. We conclude the paper by evaluating the accuracy and robustness of our model. The capability of the selected neural network to model the complex behavior of dynamical systems is indeed verified, even at the presence of noise.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114886073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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