Pub Date : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604886
Hayder Hadi Mohammed, Hassanain Ali Lafta
The present study, modeling the mechanical behavior of the bladder is very important and vital for clinical applications. All techniques do not easily success to assess the biomechanical alterations in bladder wall layers under various pressure loading conditions. In the finite element method, several hyperelastic theories are used for studying and modeling the urinary bladder extension during filling phase. All layers of the bladder have different mechanical features generate high extension when exposed to the various pressure loading. The present study computational analysis of bladder wall layers has comparable data for experimental analysis by ultrasound imaging: both shows that the study here provides further understanding of the changes in the thickness values that happen in bladder wall layers. Finite element method can be utilized as a predictive tool to determine the deformation of three layers. The detrusor muscle lessens to (1.619324) mm from (2.8) mm registering a 42.167% change in its thickness at 19kPa pressure loading. The thickness of mucosa layer lessens between (0.8-0.3) mm by 62.5% decreasing proportion. The serona layer lessens from 0.7 mm to 0.2554 mm by 63.51% decreasing proportion.
{"title":"A Combined Computational and Experimental Analysis on the Thickness of the Urinary Bladder Wall Layers during Filling Phase","authors":"Hayder Hadi Mohammed, Hassanain Ali Lafta","doi":"10.1109/ICABME53305.2021.9604886","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604886","url":null,"abstract":"The present study, modeling the mechanical behavior of the bladder is very important and vital for clinical applications. All techniques do not easily success to assess the biomechanical alterations in bladder wall layers under various pressure loading conditions. In the finite element method, several hyperelastic theories are used for studying and modeling the urinary bladder extension during filling phase. All layers of the bladder have different mechanical features generate high extension when exposed to the various pressure loading. The present study computational analysis of bladder wall layers has comparable data for experimental analysis by ultrasound imaging: both shows that the study here provides further understanding of the changes in the thickness values that happen in bladder wall layers. Finite element method can be utilized as a predictive tool to determine the deformation of three layers. The detrusor muscle lessens to (1.619324) mm from (2.8) mm registering a 42.167% change in its thickness at 19kPa pressure loading. The thickness of mucosa layer lessens between (0.8-0.3) mm by 62.5% decreasing proportion. The serona layer lessens from 0.7 mm to 0.2554 mm by 63.51% decreasing proportion.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115567769","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604876
Ghinwa Masri, H. Harb, Nadim Diab, Ramzi Halabi
Wrist-disarticulated patients face several obstacles while performing their daily tasks such as eating a meal, opening a bottle, and so on due to the fact that they have a missing hand. Therefore, the purpose of this research is to develop a smart myoelectric prosthetic hand that can perform two gestures commonly used in these patients’ daily lives: button pushing and holding a bottle (grasping). In terms of the mechanical design, several aspects were considered to study its performance, such as the weight, size, and load it can handle. Static analysis is performed based on the Von Mises equation to inspect the structural failure of the prosthetic hand and fingers. For the myoelectric control, three blind source separation (BSS) techniques including Principal Component Analysis (PCA), Empirical Mode Decomposition combined with Independent Component Analysis (EMD-ICA), and Ensemble EMD-ICA (EEMD-ICA) were applied on surface Electromyographic (EMG) data obtained from 20 healthy subjects. BSS was used for extracting three motion-specific sources. As a result, 90% was the highest supervised machine learning classification accuracy obtained from the PCA-based separation technique using Fine Gaussian Support Vector Machine (SVM). Our future work will be extended by designing and implementing a complete prosthetic arm. We will also build the kinematic model of the system for the sake of optimizing the motion. In addition, we will classify more gestures for enabling patients to do a wider variety of daily tasks.
{"title":"Design and Control of a Myoelectric Prosthetic Hand using Multi-Channel Blind Source Separation Techniques","authors":"Ghinwa Masri, H. Harb, Nadim Diab, Ramzi Halabi","doi":"10.1109/ICABME53305.2021.9604876","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604876","url":null,"abstract":"Wrist-disarticulated patients face several obstacles while performing their daily tasks such as eating a meal, opening a bottle, and so on due to the fact that they have a missing hand. Therefore, the purpose of this research is to develop a smart myoelectric prosthetic hand that can perform two gestures commonly used in these patients’ daily lives: button pushing and holding a bottle (grasping). In terms of the mechanical design, several aspects were considered to study its performance, such as the weight, size, and load it can handle. Static analysis is performed based on the Von Mises equation to inspect the structural failure of the prosthetic hand and fingers. For the myoelectric control, three blind source separation (BSS) techniques including Principal Component Analysis (PCA), Empirical Mode Decomposition combined with Independent Component Analysis (EMD-ICA), and Ensemble EMD-ICA (EEMD-ICA) were applied on surface Electromyographic (EMG) data obtained from 20 healthy subjects. BSS was used for extracting three motion-specific sources. As a result, 90% was the highest supervised machine learning classification accuracy obtained from the PCA-based separation technique using Fine Gaussian Support Vector Machine (SVM). Our future work will be extended by designing and implementing a complete prosthetic arm. We will also build the kinematic model of the system for the sake of optimizing the motion. In addition, we will classify more gestures for enabling patients to do a wider variety of daily tasks.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"785 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116414582","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604816
S. Rihana, Christelle Bou Rjeily
The current global spread of COVID-19, a highly contagious disease, has challenged healthcare systems and placed immense burdens on medical staff globally. Almost 5% to 10% among hospitalized patients will require ICU admission. Predicting ICU admission can help in managing better the patient and the healthcare system. This study aims to develop a model that can predict whether a COVID-19 patient, who has already been admitted to the hospital, will enter the ICU or not. This could be accomplished by monitoring his vital signs, and blood tests, and inquiring about his demographic records, during his stay in the hospital. Multiple models, including Artificial Neural Networks, Logistic Regression, Decision Tree, Random Forest, Gaussian Naïve Bayes, Gradient Boosting, and Support Vector Machines, were designed and implemented using MATLAB and Python. Random Forest, Decision Tree, and Gradient Boosting, are examples of decision tree-based algorithms that outperformed the others. The Random Forest (Accuracy: 99.12%, Cross-Validation Accuracy 86.34%) and Decision Tree (Accuracy: 99.12%, Cross-Validation Accuracy 79.48%) and Gradient Boosting (Accuracy: 93.77%, Cross-Validation Accuracy: 86.96%) had the highest accuracy scores as compared to other models such as the Support Vector Machines (Accuracy: 87.74%, Cross-Validation Accuracy 72.42%). In future work, the aim will be to predict whether a patient will join ICU or not, based on monitoring for multiple windows. As a result, high accuracy scores will be reached, since the model will analyze the vital signs and laboratory data at multiple stages and timings. In this way, anticipating the requirement for ICU admission well ahead of time.
{"title":"Artificial Intelligence Framework for COVID19 Patients Monitoring","authors":"S. Rihana, Christelle Bou Rjeily","doi":"10.1109/ICABME53305.2021.9604816","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604816","url":null,"abstract":"The current global spread of COVID-19, a highly contagious disease, has challenged healthcare systems and placed immense burdens on medical staff globally. Almost 5% to 10% among hospitalized patients will require ICU admission. Predicting ICU admission can help in managing better the patient and the healthcare system. This study aims to develop a model that can predict whether a COVID-19 patient, who has already been admitted to the hospital, will enter the ICU or not. This could be accomplished by monitoring his vital signs, and blood tests, and inquiring about his demographic records, during his stay in the hospital. Multiple models, including Artificial Neural Networks, Logistic Regression, Decision Tree, Random Forest, Gaussian Naïve Bayes, Gradient Boosting, and Support Vector Machines, were designed and implemented using MATLAB and Python. Random Forest, Decision Tree, and Gradient Boosting, are examples of decision tree-based algorithms that outperformed the others. The Random Forest (Accuracy: 99.12%, Cross-Validation Accuracy 86.34%) and Decision Tree (Accuracy: 99.12%, Cross-Validation Accuracy 79.48%) and Gradient Boosting (Accuracy: 93.77%, Cross-Validation Accuracy: 86.96%) had the highest accuracy scores as compared to other models such as the Support Vector Machines (Accuracy: 87.74%, Cross-Validation Accuracy 72.42%). In future work, the aim will be to predict whether a patient will join ICU or not, based on monitoring for multiple windows. As a result, high accuracy scores will be reached, since the model will analyze the vital signs and laboratory data at multiple stages and timings. In this way, anticipating the requirement for ICU admission well ahead of time.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124953579","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604854
Jinan Charafeddine, Ibrahim Maassarani, S. Chevallier, S. Alfayad
Cerebral Palsy (CP) is a debilitating neurological disorder that reduces motor function for children with CP. This paper presents the latest trends in the development of the arm exoskeleton for children afflicted by CP. Furthermore, it discusses the prospects for achieving an optimal outcome in rehabilitation and assistance. Nine upper limb exoskeletons, which targeted CP-afflicted children and were developed in recent years, are presented. Three of these exoskeletons are most commonly used in rehabilitation, and the other six are used for assistive purposes. Henceforth, it discusses when CP-afflicted children can make good use of this rehabilitation or assistance. In conclusion, this research should focus on a scalable upper limb exoskeleton that would benefit the majority of children afflicted by CP.
{"title":"Purposeful Proposal for CP-afflicted Upper Limbs Exoskeletons","authors":"Jinan Charafeddine, Ibrahim Maassarani, S. Chevallier, S. Alfayad","doi":"10.1109/ICABME53305.2021.9604854","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604854","url":null,"abstract":"Cerebral Palsy (CP) is a debilitating neurological disorder that reduces motor function for children with CP. This paper presents the latest trends in the development of the arm exoskeleton for children afflicted by CP. Furthermore, it discusses the prospects for achieving an optimal outcome in rehabilitation and assistance. Nine upper limb exoskeletons, which targeted CP-afflicted children and were developed in recent years, are presented. Three of these exoskeletons are most commonly used in rehabilitation, and the other six are used for assistive purposes. Henceforth, it discusses when CP-afflicted children can make good use of this rehabilitation or assistance. In conclusion, this research should focus on a scalable upper limb exoskeleton that would benefit the majority of children afflicted by CP.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128367870","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604845
Fen Xia, M. Sawan
Obstructive sleep apnea (OSA) is a common breathing disorder affecting around one in seven individuals in the world. Electrical stimulation of respiration neural pathways can be a treatment alternative to enhance patients’ conditions. Hypoglossal nerve stimulation is an emerging management of OSA. While a few studies have explored this avenue, and a single implantable system is commercially available, additional effort is required to achieve clinical expectations. Hypoglossal nerve can be stimulated with electrode embedded in a cuff format encircling the nerve. To maximize the benefit of neural stimulation, the electrode-hypoglossal nerve interface (EHNI) impedance should be analyzed. In this study, a precise hypoglossal nerve model is built with inhomogeneous conductivity in COMSOL. The simulation results provided the properties of the EHNI impedance in its two parts resistive and capacitance. The parameters of the EHNI are associated with the size of the electrode’s interfaces. The smaller size of these interfaces, the higher the impedance and the lower of its capacitance. These results bring knowledge to build efficient implantable devices in total implant’s volume and in its energy consumption.
{"title":"Electrode-Nerve Interface Properties to Treat Patients with OSA through Electrical Stimulation","authors":"Fen Xia, M. Sawan","doi":"10.1109/ICABME53305.2021.9604845","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604845","url":null,"abstract":"Obstructive sleep apnea (OSA) is a common breathing disorder affecting around one in seven individuals in the world. Electrical stimulation of respiration neural pathways can be a treatment alternative to enhance patients’ conditions. Hypoglossal nerve stimulation is an emerging management of OSA. While a few studies have explored this avenue, and a single implantable system is commercially available, additional effort is required to achieve clinical expectations. Hypoglossal nerve can be stimulated with electrode embedded in a cuff format encircling the nerve. To maximize the benefit of neural stimulation, the electrode-hypoglossal nerve interface (EHNI) impedance should be analyzed. In this study, a precise hypoglossal nerve model is built with inhomogeneous conductivity in COMSOL. The simulation results provided the properties of the EHNI impedance in its two parts resistive and capacitance. The parameters of the EHNI are associated with the size of the electrode’s interfaces. The smaller size of these interfaces, the higher the impedance and the lower of its capacitance. These results bring knowledge to build efficient implantable devices in total implant’s volume and in its energy consumption.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116654951","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604819
M. Abbas, D. Somme, R. Le Bouquin Jeannès
This paper investigates the possibility of predicting physical weakening in older adults in view to detect early the on-set of frailty process. This study is based on two types of features, namely (i) measured features, which are calculated objectively using performance tests and questionnaires, and (ii) self-reported features, which are based on the older person’s auto-evaluation. Two machine learning-based models are proposed. The first one identifies the potential occurrence of physical weakening by comparing the evolution of the aforementioned features between two time slots. The second one predicts a future worsening based on the current values of these features. Both models are evaluated and interpreted using a public dataset.
{"title":"Identifying Physical Worsening in Elderly Using Objective and Self-Reported Measures","authors":"M. Abbas, D. Somme, R. Le Bouquin Jeannès","doi":"10.1109/ICABME53305.2021.9604819","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604819","url":null,"abstract":"This paper investigates the possibility of predicting physical weakening in older adults in view to detect early the on-set of frailty process. This study is based on two types of features, namely (i) measured features, which are calculated objectively using performance tests and questionnaires, and (ii) self-reported features, which are based on the older person’s auto-evaluation. Two machine learning-based models are proposed. The first one identifies the potential occurrence of physical weakening by comparing the evolution of the aforementioned features between two time slots. The second one predicts a future worsening based on the current values of these features. Both models are evaluated and interpreted using a public dataset.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116790740","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604898
Joseph Babayan, Markus Lueken, Arun Berking, A. Pickartz, K. Reetz, F. Holtbernd, S. Leonhardt, C. Ngo
Parkinson’s disease is a neurological disorder characterized by the deficiency of dopamine levels in the brain. More than 75 percent of these patients suffer from tremors. Parkinsonian tremor (PT) is more characterized to be a rest tremor, but some patients suffer from action tremor as well. Usually, patients suffering from this disease are diagnosed by their physicians who perform some battery MDS-UPDRS tasks to determine the disorder. Some sensors were used to diagnose the tremor objectively, but in this study, we are using a new Body Sensor Network (BSN) designed at our institute to be used in detecting the acceleration, gyroscope, and magnetometer of the tremor patients in the clinic. Signal processing of the recorded data is performed to determine and classify the number of times throughout the day the patient suffered from tremors. This is ensured through automatic signal segmentation, extraction of several signal features, and classification with the most accurate machine learning classifier. In this study, we have proved that our BSN sensor is capable of helping clinicians in classifying tremor occurrence in Parkinson diseased patients specifically, and tremor patients generally throughout monitoring their everyday life activities.
{"title":"Everyday Life Tremor Signal Processing in PD Patients using BSN","authors":"Joseph Babayan, Markus Lueken, Arun Berking, A. Pickartz, K. Reetz, F. Holtbernd, S. Leonhardt, C. Ngo","doi":"10.1109/ICABME53305.2021.9604898","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604898","url":null,"abstract":"Parkinson’s disease is a neurological disorder characterized by the deficiency of dopamine levels in the brain. More than 75 percent of these patients suffer from tremors. Parkinsonian tremor (PT) is more characterized to be a rest tremor, but some patients suffer from action tremor as well. Usually, patients suffering from this disease are diagnosed by their physicians who perform some battery MDS-UPDRS tasks to determine the disorder. Some sensors were used to diagnose the tremor objectively, but in this study, we are using a new Body Sensor Network (BSN) designed at our institute to be used in detecting the acceleration, gyroscope, and magnetometer of the tremor patients in the clinic. Signal processing of the recorded data is performed to determine and classify the number of times throughout the day the patient suffered from tremors. This is ensured through automatic signal segmentation, extraction of several signal features, and classification with the most accurate machine learning classifier. In this study, we have proved that our BSN sensor is capable of helping clinicians in classifying tremor occurrence in Parkinson diseased patients specifically, and tremor patients generally throughout monitoring their everyday life activities.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411232","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604905
Yasmine A. Abu Adla, Dalia G. Raydan, Mohammad-Zafer J. Charaf, Roua A. Saad, J. Nasreddine, Mohammad O. Diab
Polycystic Ovary Syndrome (PCOS) is a medical condition affecting the female’s reproductive system causing ano/oligoovulation, hyperandrogenism, and/or polycystic ovaries. Due to the complexities in diagnosing this disorder, it was of upmost importance to find a solution to assist physicians with this process. Therefore, in this study, we investigated the possibility of building a model that aims to automate the diagnosis of PCOS using Machine Learning (ML) algorithms and techniques. In this context, a dataset that consisted of 39 features ranging from metabolic, imaging, to hormonal and biochemical parameters for 541 subjects was used. First, we applied pre-processing on the data. Hereafter, a hybrid feature selection approach was implemented to reduce the number of features using filters and wrappers. Different classification algorithms were then trained and evaluated. Based on a thorough analysis, the Support Vector Machine with a Linear kernel (Linear SVM) was chosen, as it performed best among the others in terms of precision (93.665%) as well as high accuracy (91.6%) and recall (80.6%).
{"title":"Automated Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques","authors":"Yasmine A. Abu Adla, Dalia G. Raydan, Mohammad-Zafer J. Charaf, Roua A. Saad, J. Nasreddine, Mohammad O. Diab","doi":"10.1109/ICABME53305.2021.9604905","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604905","url":null,"abstract":"Polycystic Ovary Syndrome (PCOS) is a medical condition affecting the female’s reproductive system causing ano/oligoovulation, hyperandrogenism, and/or polycystic ovaries. Due to the complexities in diagnosing this disorder, it was of upmost importance to find a solution to assist physicians with this process. Therefore, in this study, we investigated the possibility of building a model that aims to automate the diagnosis of PCOS using Machine Learning (ML) algorithms and techniques. In this context, a dataset that consisted of 39 features ranging from metabolic, imaging, to hormonal and biochemical parameters for 541 subjects was used. First, we applied pre-processing on the data. Hereafter, a hybrid feature selection approach was implemented to reduce the number of features using filters and wrappers. Different classification algorithms were then trained and evaluated. Based on a thorough analysis, the Support Vector Machine with a Linear kernel (Linear SVM) was chosen, as it performed best among the others in terms of precision (93.665%) as well as high accuracy (91.6%) and recall (80.6%).","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121103816","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604900
Kamar Chehimy, Ramzi Halabi, M. Diab, Mahmoud Hassan, A. Mheich
Brain network analysis is an interdisciplinary field linking computational neuroscience with biomedical data analytics, aiming for instance to map the brain into interconnected regions at different conditions, resting versus inactivity, and normal versus pathological. In our study, brain connectivity modeling and analysis are performed via graph theory. Several studies have revealed alterations in structural/functional brain networks of people diagnosed with several brain disorders. Most of the studies in the literature used graph theoretical approaches to characterize these disorders, however less attention was given for distance-based approaches (or network similarity). Our objective here is to compare the brain networks of normal versus Alzheimer’s disease (AD) patients by performing distance-based graph similarity analysis between their electrophysiological brain networks. The brain networks of a group of 10 healthy control subjects and 10 AD patients were constructed from Electroencephalography (EEG) signals recorded at rest, followed by the computation of intra- and inter-group network similarity via Siminet and DeltaCon algorithms at the EEG alpha and beta frequency bands. Results showed that AD networks have significantly lower similarity scores and tend to be more heterogenous with respect to the healthy networks. This work provides a preliminary foundation for the effective use of graph similarity in the computational assessment of pathological brain networks compared to healthy subjects.
{"title":"Comparing Healthy Subjects and Alzheimer’s Disease Patients using Brain Network Similarity: a Preliminary Study","authors":"Kamar Chehimy, Ramzi Halabi, M. Diab, Mahmoud Hassan, A. Mheich","doi":"10.1109/ICABME53305.2021.9604900","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604900","url":null,"abstract":"Brain network analysis is an interdisciplinary field linking computational neuroscience with biomedical data analytics, aiming for instance to map the brain into interconnected regions at different conditions, resting versus inactivity, and normal versus pathological. In our study, brain connectivity modeling and analysis are performed via graph theory. Several studies have revealed alterations in structural/functional brain networks of people diagnosed with several brain disorders. Most of the studies in the literature used graph theoretical approaches to characterize these disorders, however less attention was given for distance-based approaches (or network similarity). Our objective here is to compare the brain networks of normal versus Alzheimer’s disease (AD) patients by performing distance-based graph similarity analysis between their electrophysiological brain networks. The brain networks of a group of 10 healthy control subjects and 10 AD patients were constructed from Electroencephalography (EEG) signals recorded at rest, followed by the computation of intra- and inter-group network similarity via Siminet and DeltaCon algorithms at the EEG alpha and beta frequency bands. Results showed that AD networks have significantly lower similarity scores and tend to be more heterogenous with respect to the healthy networks. This work provides a preliminary foundation for the effective use of graph similarity in the computational assessment of pathological brain networks compared to healthy subjects.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115365806","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 : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604842
A. Raison, P. Bourdon, C. Habas, D. Helbert
Artificial Intelligence, especially deep neural networks, have shown impressive performances for classification tasks since the last decade. In the medical field, trustworthy deep models exist but they do not provide any insights on how and why they classify data due to their complex structure. In this study we propose to leverage the power of deep neural network for classifying resting state brain activities by gender, then we use explainable Artificial Intelligence models to determine which functional networks are salient with respect to the gender. Firstly, we trained an accurate convolutional neural network to determine gender based on resting-state brain spatial maps corresponding to intrinsically connected networks and computed by independent component analysis. Then, we compare, through mask-based assessment, state of the art explainable Artificial Intelligence models to extract the most meaningful components involved in gender determination. Based on a powerful deep classifier, and with an appropriate explainable artificial intelligence method, we supply meaningful results in accordance with neurology literature results for gender classification. Throughout this study, we show that powerful deep models can be used in medical diagnostics since they recover, thank to reliable explainable artificial intelligence models, already established literature results related to gender determination with respect to brain network activities.
{"title":"Explicability in resting-state fMRI for gender classification","authors":"A. Raison, P. Bourdon, C. Habas, D. Helbert","doi":"10.1109/ICABME53305.2021.9604842","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604842","url":null,"abstract":"Artificial Intelligence, especially deep neural networks, have shown impressive performances for classification tasks since the last decade. In the medical field, trustworthy deep models exist but they do not provide any insights on how and why they classify data due to their complex structure. In this study we propose to leverage the power of deep neural network for classifying resting state brain activities by gender, then we use explainable Artificial Intelligence models to determine which functional networks are salient with respect to the gender. Firstly, we trained an accurate convolutional neural network to determine gender based on resting-state brain spatial maps corresponding to intrinsically connected networks and computed by independent component analysis. Then, we compare, through mask-based assessment, state of the art explainable Artificial Intelligence models to extract the most meaningful components involved in gender determination. Based on a powerful deep classifier, and with an appropriate explainable artificial intelligence method, we supply meaningful results in accordance with neurology literature results for gender classification. Throughout this study, we show that powerful deep models can be used in medical diagnostics since they recover, thank to reliable explainable artificial intelligence models, already established literature results related to gender determination with respect to brain network activities.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129499852","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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