Pub Date : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052862
Mohammad Hosein Zadeh Posti, Mohadese Rajaeirad, Aisan Rafie, H. A. Gilakjani, M. Khorsandi
Knee replacement surgery is a common treatment for patients with end-stage knee arthrosis. Unfortunately, the age of patients suffering from this condition and requiring knee joint replacement is decreasing, leading to an increase in the need for revision surgeries. Therefore, using suitable prostheses can increase the durability of joint replacement and the success of this procedure. For this purpose, in this research, three materials, Co-Cr, Ti6AI4V, and FGM, were examined by the finite element method. An accurate three-dimensional model of the distal part of the femur bone was developed, and after designing the stem, its volume was subtracted from the bone, and the final assembly model and material properties were assigned to it. The models were subjected to jogging (6Km/h) and walking loading conditions. The findings of this research showed that in both loading conditions, the Co-Cr stem was subjected to stress more than twice the stress applied to the other two stems. Also, jogging applies more stress to the stem-bone construct than walking. According to the results of this research, it can be said that the use of FGM and Ti6A14V stems is preferable, although the construction costs should also be considered. In addition, examining more active movements can be influential in determining movement limitations after revision surgery.
{"title":"Stress Distribution in Femoral Stems for Revision Total Knee Arthroplasty with Three Different Materials: A Comparative Finite Element Study","authors":"Mohammad Hosein Zadeh Posti, Mohadese Rajaeirad, Aisan Rafie, H. A. Gilakjani, M. Khorsandi","doi":"10.1109/ICBME57741.2022.10052862","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052862","url":null,"abstract":"Knee replacement surgery is a common treatment for patients with end-stage knee arthrosis. Unfortunately, the age of patients suffering from this condition and requiring knee joint replacement is decreasing, leading to an increase in the need for revision surgeries. Therefore, using suitable prostheses can increase the durability of joint replacement and the success of this procedure. For this purpose, in this research, three materials, Co-Cr, Ti6AI4V, and FGM, were examined by the finite element method. An accurate three-dimensional model of the distal part of the femur bone was developed, and after designing the stem, its volume was subtracted from the bone, and the final assembly model and material properties were assigned to it. The models were subjected to jogging (6Km/h) and walking loading conditions. The findings of this research showed that in both loading conditions, the Co-Cr stem was subjected to stress more than twice the stress applied to the other two stems. Also, jogging applies more stress to the stem-bone construct than walking. According to the results of this research, it can be said that the use of FGM and Ti6A14V stems is preferable, although the construction costs should also be considered. In addition, examining more active movements can be influential in determining movement limitations after revision surgery.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125864387","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052956
Sepide Banihashem Nejad, Nima Hashemi, Ershad Hasanpour, F. Jalousian, S. Jamshidi, Seyed Hossein Hosseini, Fatemeh Manshori Ghaishghorshagh, H. Soltanian-Zadeh
Dirofilaria immitis (D. immitis) or Heartworm is the most pathogenic filariae in dogs which also occasionally infects humans. Dirofilariasis has been found all over the world, and in Iran, on average, 11.5% of dogs are infected. Microscopic examination, the modified Knott method, is a definitive and very common diagnosis method for detecting microfilariae in peripheral blood. It is inexpensive, relatively quick, and does not require advanced and expensive laboratory equipment. However, identification and differentiation of microfilariae from artifacts stand on the abilities and expertise of technicians. The aim of this study was to remove this limitation by developing an artificial intelligence, deep learning-based system that detects microfilariae in blood slides and differentiates microfilaria from thread-like artifacts automatically. To this end, blood samples (n=300) were obtained from stray dogs in Guilan province. The existence of microfilariae was assessed by modified Knott's test under microscopic examinations which identified 29 cases infected with microfilaria. These positive results were confirmed with conventional PCR. The Microfilariae measuring found 295.13±14.9 µm in length and 5.8±0.43 µm in width. The images captured of microfilariae and artifacts were applied to educate and test the suggested deep learning-based system. The developed system diagnoses D. immitis with an accuracy of greater than 95% and thus, can be widely used for epidemiological studies. Since the microfilariae can be miss-diagnosed with thread-shaped artifacts, the proposed system plays an effective role in accurate and reliable diagnosis of D. immitis and can be used in field studies.
{"title":"Deep learning-based diagnosis of Dirofilaria immitis microfilariae in dog blood","authors":"Sepide Banihashem Nejad, Nima Hashemi, Ershad Hasanpour, F. Jalousian, S. Jamshidi, Seyed Hossein Hosseini, Fatemeh Manshori Ghaishghorshagh, H. Soltanian-Zadeh","doi":"10.1109/ICBME57741.2022.10052956","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052956","url":null,"abstract":"Dirofilaria immitis (D. immitis) or Heartworm is the most pathogenic filariae in dogs which also occasionally infects humans. Dirofilariasis has been found all over the world, and in Iran, on average, 11.5% of dogs are infected. Microscopic examination, the modified Knott method, is a definitive and very common diagnosis method for detecting microfilariae in peripheral blood. It is inexpensive, relatively quick, and does not require advanced and expensive laboratory equipment. However, identification and differentiation of microfilariae from artifacts stand on the abilities and expertise of technicians. The aim of this study was to remove this limitation by developing an artificial intelligence, deep learning-based system that detects microfilariae in blood slides and differentiates microfilaria from thread-like artifacts automatically. To this end, blood samples (n=300) were obtained from stray dogs in Guilan province. The existence of microfilariae was assessed by modified Knott's test under microscopic examinations which identified 29 cases infected with microfilaria. These positive results were confirmed with conventional PCR. The Microfilariae measuring found 295.13±14.9 µm in length and 5.8±0.43 µm in width. The images captured of microfilariae and artifacts were applied to educate and test the suggested deep learning-based system. The developed system diagnoses D. immitis with an accuracy of greater than 95% and thus, can be widely used for epidemiological studies. Since the microfilariae can be miss-diagnosed with thread-shaped artifacts, the proposed system plays an effective role in accurate and reliable diagnosis of D. immitis and can be used in field studies.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126501561","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052843
Elahé Yargholi, Sepideh Allahdadian, Hossein Rafipoor, M. Mirian, Saurabh Garg, Linlin Gao, M. McKeown
Alterations of amygdala function in Parkinson's Disease (PD) are associated with emotion-related clinical features such as impaired facial recognition, but the effects on motor performance in an emotionally-neutral task are unclear. We studied fMRI from healthy and PD subjects while they squeezed a rubber bulb to keep a bar within two parallel “tracks” that were scrolling downward. At discrete intervals, there were bifurcations of each track, and the subject had to follow either the inside or outside track requiring squeezing at 5% or 15% of maximum voluntary contraction. During the control condition (Control), subjects had to follow the inside and outside tracks alternately. In the timing (Timing) and selection (Selection) tasks, the time between bifurcations jittered randomly and the color of the bar determined which path to choose, respectively. We determined which Regions of Interest (ROIs) were activated at the time of bifurcations, by assessing both the connectivity between ROIs and the timing of activation. The caudate and putamen were activated in both (Selection-Control) and (Timing-Control) contrasts in all subjects, however only in PD subjects was the amygdala significantly activated. In addition, the amygdala was activated faster in both Selection and Timing tasks compared to the Control task in PD subjects. In PD subjects, the greatest connectivity was to/from the amygdala, while in healthy subjects the strongest connectivity was seen between the caudate and putamen. Our results suggest that PD subjects recruit the amygdala to maintain performance in motor timing and program selection even during emotionally-neutral tasks.
{"title":"Compensatory Role of the Amygdala During Motor Timing and Selection in Parkinson's Disease","authors":"Elahé Yargholi, Sepideh Allahdadian, Hossein Rafipoor, M. Mirian, Saurabh Garg, Linlin Gao, M. McKeown","doi":"10.1109/ICBME57741.2022.10052843","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052843","url":null,"abstract":"Alterations of amygdala function in Parkinson's Disease (PD) are associated with emotion-related clinical features such as impaired facial recognition, but the effects on motor performance in an emotionally-neutral task are unclear. We studied fMRI from healthy and PD subjects while they squeezed a rubber bulb to keep a bar within two parallel “tracks” that were scrolling downward. At discrete intervals, there were bifurcations of each track, and the subject had to follow either the inside or outside track requiring squeezing at 5% or 15% of maximum voluntary contraction. During the control condition (Control), subjects had to follow the inside and outside tracks alternately. In the timing (Timing) and selection (Selection) tasks, the time between bifurcations jittered randomly and the color of the bar determined which path to choose, respectively. We determined which Regions of Interest (ROIs) were activated at the time of bifurcations, by assessing both the connectivity between ROIs and the timing of activation. The caudate and putamen were activated in both (Selection-Control) and (Timing-Control) contrasts in all subjects, however only in PD subjects was the amygdala significantly activated. In addition, the amygdala was activated faster in both Selection and Timing tasks compared to the Control task in PD subjects. In PD subjects, the greatest connectivity was to/from the amygdala, while in healthy subjects the strongest connectivity was seen between the caudate and putamen. Our results suggest that PD subjects recruit the amygdala to maintain performance in motor timing and program selection even during emotionally-neutral tasks.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927321","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052897
Soheil Khooyooz, S. H. Sardouie
Brain-Computer Interface (BCI) systems establish a control and communication relationship between the human brain and computers including robots or other devices to help individuals with severe motor disabilities. The classification of motor and mental imagery electroencephalogram (EEG) signals is complicated because these signals are usually case-specific and distinct models must be trained for each subject to process and classify his/her EEG signals. Moreover, in BCI systems EEG signals are processed online, so the time latency must be very low. In this paper, we have proposed a method based on signal-to-image conversion to investigate image processing techniques in the pair-wise classification of motor and mental imagery EEG signals. We first decomposed EEG signals of each trial into four sub-bands. Then, for each sub-band, we converted EEG time series to 2-dimensional (2D) images using covariance between signals of all channels. Then, statistical, textural and PCA-based features were extracted from these images and fed to a support vector machine (SVM) classifier. Our results were promising in the offline processing and achieved an average classification accuracy of 79.57%.
{"title":"Classification of Motor and Mental Imagery EEG Signals in BCI Systems Based on Signal-to-Image Conversion","authors":"Soheil Khooyooz, S. H. Sardouie","doi":"10.1109/ICBME57741.2022.10052897","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052897","url":null,"abstract":"Brain-Computer Interface (BCI) systems establish a control and communication relationship between the human brain and computers including robots or other devices to help individuals with severe motor disabilities. The classification of motor and mental imagery electroencephalogram (EEG) signals is complicated because these signals are usually case-specific and distinct models must be trained for each subject to process and classify his/her EEG signals. Moreover, in BCI systems EEG signals are processed online, so the time latency must be very low. In this paper, we have proposed a method based on signal-to-image conversion to investigate image processing techniques in the pair-wise classification of motor and mental imagery EEG signals. We first decomposed EEG signals of each trial into four sub-bands. Then, for each sub-band, we converted EEG time series to 2-dimensional (2D) images using covariance between signals of all channels. Then, statistical, textural and PCA-based features were extracted from these images and fed to a support vector machine (SVM) classifier. Our results were promising in the offline processing and achieved an average classification accuracy of 79.57%.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130693830","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10053063
M. Estaji, M. Nabaei, A. Farnoud
Computational fluid dynamics (CFD) modeling of airflow through respiratory airways is the basis of particle delivery studies. Lots of researches have been done in this area. Computed tomography (CT) scan and magnetic resonance imaging (MRI) images are the most conventional techniques for 3-dimensional (3D) modeling of airways. Because of the resolution limits, these techniques are not efficient for modeling high order airways. In present study high resolution Light sheet fluorescent microscopy (LSFM) images of a mouse respiratory system were used to construct a 3D model of the left-lobe airways. This model includes the whole conducting zone of mice left-lobe. Then the air flow through the airways was modeled considering rigid walls and a steady state condition for airflow. Pressure and wall shear stress distribution in the airways were obtained beside the velocity and vorticity profiles near the entrance of each airway order.
{"title":"CFD modeling of airflow in a realistic model of a mouse left-lobe respiratory airways","authors":"M. Estaji, M. Nabaei, A. Farnoud","doi":"10.1109/ICBME57741.2022.10053063","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10053063","url":null,"abstract":"Computational fluid dynamics (CFD) modeling of airflow through respiratory airways is the basis of particle delivery studies. Lots of researches have been done in this area. Computed tomography (CT) scan and magnetic resonance imaging (MRI) images are the most conventional techniques for 3-dimensional (3D) modeling of airways. Because of the resolution limits, these techniques are not efficient for modeling high order airways. In present study high resolution Light sheet fluorescent microscopy (LSFM) images of a mouse respiratory system were used to construct a 3D model of the left-lobe airways. This model includes the whole conducting zone of mice left-lobe. Then the air flow through the airways was modeled considering rigid walls and a steady state condition for airflow. Pressure and wall shear stress distribution in the airways were obtained beside the velocity and vorticity profiles near the entrance of each airway order.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127414470","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052798
A. Ghafari, Javad Loghmani
The main scope of this article is to present design and fabrication of a simple active robotic prosthetics thumb which is activated by the motion of the index finger. In the proposed single degree-of-freedom mechanism output signal of the strain gauge attached to the index finger is utilized to activate the driving mechanism of the assistive robotic thumb. Stepper motor is employed as a driving system in the proposed mechanism. Experimental investigation was performed to evaluate the performance of the constructed prototype for catching, grasping, and lifting activities of various objects with different weights and shapes. The experimental results indicate very good performance of the proposed artificial thumb prototype and illustrate the assistive system's action is very compliant to the motion of the able-bodied hand. As a result, it can be mentioned that the key factor for successful design of a portable in-home robotic-assistive prosthetics is to consider the anatomy compliancy in actuation system guarantees the opportunity of successful post-stoke treatment.
{"title":"Design and Construction of an Artificial Thumb Prosthetics Controlled by Index Finger","authors":"A. Ghafari, Javad Loghmani","doi":"10.1109/ICBME57741.2022.10052798","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052798","url":null,"abstract":"The main scope of this article is to present design and fabrication of a simple active robotic prosthetics thumb which is activated by the motion of the index finger. In the proposed single degree-of-freedom mechanism output signal of the strain gauge attached to the index finger is utilized to activate the driving mechanism of the assistive robotic thumb. Stepper motor is employed as a driving system in the proposed mechanism. Experimental investigation was performed to evaluate the performance of the constructed prototype for catching, grasping, and lifting activities of various objects with different weights and shapes. The experimental results indicate very good performance of the proposed artificial thumb prototype and illustrate the assistive system's action is very compliant to the motion of the able-bodied hand. As a result, it can be mentioned that the key factor for successful design of a portable in-home robotic-assistive prosthetics is to consider the anatomy compliancy in actuation system guarantees the opportunity of successful post-stoke treatment.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350632","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052993
A. Ghafari, Elnaz Azizi
In the last few years, Brain Computer Interfaces (BCI) attempted the attention of many researchers. In Motor Imagery (MI)-BCI, central nervous system directly connected to a computer or an automation system. Characteristics of the electromyographic (EEG) signals are utilized in MI-BCI systems. Various techniques have been proposed to extract EEG signal characteristics during recent years. The main objective of this research is to employ an efficient deep learning approach to extract the features of EEG signals using composition of convolutional Neural Network and discrete wavelet transform utilized in the BCI system. The deep learning approach presented in this study has rarely been explored to employ for EEG features extraction. The simulation study indicates that the presented method carry out remarkable accuracy and high performance compared with conventional approaches such as support vector machine and artificial Neural Network methods and give a powerful indicative decision making aid to assist physicians in the treatment of the right and left-hand features for real time motor imagery classification system. Furthermore, the most advantages of employing the proposed method are to eliminate the feature selection level and reducing the processing cost significantly.
{"title":"Employing Deep Learning and Discrete Wavelet Transform Approach to Classify Motor Imagery Based Brain Computer Interface System","authors":"A. Ghafari, Elnaz Azizi","doi":"10.1109/ICBME57741.2022.10052993","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052993","url":null,"abstract":"In the last few years, Brain Computer Interfaces (BCI) attempted the attention of many researchers. In Motor Imagery (MI)-BCI, central nervous system directly connected to a computer or an automation system. Characteristics of the electromyographic (EEG) signals are utilized in MI-BCI systems. Various techniques have been proposed to extract EEG signal characteristics during recent years. The main objective of this research is to employ an efficient deep learning approach to extract the features of EEG signals using composition of convolutional Neural Network and discrete wavelet transform utilized in the BCI system. The deep learning approach presented in this study has rarely been explored to employ for EEG features extraction. The simulation study indicates that the presented method carry out remarkable accuracy and high performance compared with conventional approaches such as support vector machine and artificial Neural Network methods and give a powerful indicative decision making aid to assist physicians in the treatment of the right and left-hand features for real time motor imagery classification system. Furthermore, the most advantages of employing the proposed method are to eliminate the feature selection level and reducing the processing cost significantly.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623297","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10053068
Soroosh Golbabaei, Negar Sammaknejad, K. Borhani
Difficulty in empathy is thought to be one of the problems in people with autism spectrum disorder (ASD), leading to impairment in social abilities and communication. However, despite the recent evidence on the effect of physiological bodily states on affective experiences, the exact role of physiological signals on different aspects of empathy (i.e., cognitive and affective empathy), as well as empathy dysfunction in ASD is yet unknown. To tackle this problem, in this study, 36 neurotypical subjects with different levels of autistic traits, participated in a well-established empathy for pain task, while Electrocardiogram (ECG) and Skin Conductance (SC) signals were recorded. Several features were extracted from each signal. Our results indicated that both cognitive and affective empathy are positively related to a higher level of cardiac activity (e.g., negative correlation with R-R interval) and arousal (e.g., positive correlation with average SC). More importantly, higher level of autistic traits, measured with Autism Quotient (AQ), was negatively correlated with Heart Rate Variability as measured with HRV-RMSSD and variability in tonic SC. Finally, we classified the participants into groups with high and low cognitive empathy, affective empathy, and level of autistic traits and investigated the extent to which machine learning approaches can automatically classify participants based on ECG and SC extracted features. Using a Support Vector Machine, reasonable results were obtained (in the range of. 73 to. 84), proving the possibility of implementing automatic detection systems for classifying subjects with different levels of autistic traits. Our results are suggestive of the effect of bodily simulation on empathy, and how the inability to regulate physiological signals leads to empathy dysfunction in individuals with high autistic traits.
{"title":"Physiological Indicators of The Relation Between Autistic Traits and Empathy: Evidence From Electrocardiogram and Skin Conductance Signals","authors":"Soroosh Golbabaei, Negar Sammaknejad, K. Borhani","doi":"10.1109/ICBME57741.2022.10053068","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10053068","url":null,"abstract":"Difficulty in empathy is thought to be one of the problems in people with autism spectrum disorder (ASD), leading to impairment in social abilities and communication. However, despite the recent evidence on the effect of physiological bodily states on affective experiences, the exact role of physiological signals on different aspects of empathy (i.e., cognitive and affective empathy), as well as empathy dysfunction in ASD is yet unknown. To tackle this problem, in this study, 36 neurotypical subjects with different levels of autistic traits, participated in a well-established empathy for pain task, while Electrocardiogram (ECG) and Skin Conductance (SC) signals were recorded. Several features were extracted from each signal. Our results indicated that both cognitive and affective empathy are positively related to a higher level of cardiac activity (e.g., negative correlation with R-R interval) and arousal (e.g., positive correlation with average SC). More importantly, higher level of autistic traits, measured with Autism Quotient (AQ), was negatively correlated with Heart Rate Variability as measured with HRV-RMSSD and variability in tonic SC. Finally, we classified the participants into groups with high and low cognitive empathy, affective empathy, and level of autistic traits and investigated the extent to which machine learning approaches can automatically classify participants based on ECG and SC extracted features. Using a Support Vector Machine, reasonable results were obtained (in the range of. 73 to. 84), proving the possibility of implementing automatic detection systems for classifying subjects with different levels of autistic traits. Our results are suggestive of the effect of bodily simulation on empathy, and how the inability to regulate physiological signals leads to empathy dysfunction in individuals with high autistic traits.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127103430","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10052909
Anis Allahdinian, F. Eskandari, M. Shafieian
According to the available statistics, brain injury and concussion have been the most common causes of death in recent years. Progress in biomechanics has led to the recognition of many of the current limitations and various advantages in diagnosis and treatment planning, especially in surgeries. Evaluation of different characteristics of brain tissue under mechanical loading has led to a better understanding of the mechanisms of traumatic injuries. In this study, we used a microstructural finite element approach to investigate the contribution of the tissue components to the mechanical behavior of white matter. Axons and extracellular matrix (ECM) were assumed as hyperelastic materials, and glial cells connected axons together were depicted via a spring-dashpot model. Dirichlet boundary conditions were applied to the model to evaluate the effect of the presence of glial cells in different tension and compression loading scenarios. The results showed that the presence of glial cells can change the tissue stiffness compared to their absence. Accordingly, it could be suggested that changes in the mechanical properties of injured brain tissue can be attributed to the contribution of glial cells to the mechanical behavior of brain tissue.
{"title":"The Role of Glial Cells in the Mechanical Behavior of Brain Tissue: A Mechanobiological Approach","authors":"Anis Allahdinian, F. Eskandari, M. Shafieian","doi":"10.1109/ICBME57741.2022.10052909","DOIUrl":"https://doi.org/10.1109/ICBME57741.2022.10052909","url":null,"abstract":"According to the available statistics, brain injury and concussion have been the most common causes of death in recent years. Progress in biomechanics has led to the recognition of many of the current limitations and various advantages in diagnosis and treatment planning, especially in surgeries. Evaluation of different characteristics of brain tissue under mechanical loading has led to a better understanding of the mechanisms of traumatic injuries. In this study, we used a microstructural finite element approach to investigate the contribution of the tissue components to the mechanical behavior of white matter. Axons and extracellular matrix (ECM) were assumed as hyperelastic materials, and glial cells connected axons together were depicted via a spring-dashpot model. Dirichlet boundary conditions were applied to the model to evaluate the effect of the presence of glial cells in different tension and compression loading scenarios. The results showed that the presence of glial cells can change the tissue stiffness compared to their absence. Accordingly, it could be suggested that changes in the mechanical properties of injured brain tissue can be attributed to the contribution of glial cells to the mechanical behavior of brain tissue.","PeriodicalId":319196,"journal":{"name":"2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129939726","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 : 2022-12-21DOI: 10.1109/ICBME57741.2022.10053066
Kvan Jelodare, M. Khakbiza
In the past, many people lost their lives due to superficial wound infections. Over time, the importance of wound healing as the reconstruction of the body's first defense barrier, the skin, became more important. Today, traditional medicine has given way to reconstructive medicine. Tissue engineering has always been a pioneer in the development and application of new methods as the main branch of reconstructive medicine. In this research, a Nano-Scaffold was designed and fabricated for skin Tissue engineering with unique properties using electrospinning method. In this project, to increase the function of the scaffold, two nanoparticles containing two types of drugs have been used, which is considered as a hybrid drug delivery system. Concomitant use of layered double hydroxide hybrid (LDH) nanoparticles containing curcumin and imidazole zeolite (ZIF-8) containing aspirin in poly-lactic acid scaffolding will accelerate wound healing and reduce inflammation. After processing two layered double hydroxide nanoparticles containing curcumin and imidazole zeolite containing aspirin, these two nanoparticles were loaded on poly-lactic acid nanofibers. By adding 3% by weight of nanoparticles to poly-lactic acid, the tensile strength increased from 1.31 MPa to 1.6 MPa, the contact angle increased from 59 ° to 120 ° and cell viability (within 72 hours) increased from 49% to 88%. Tests performed on the scaffold confirmed its biocompatibility.
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