Pub Date : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604829
Kinana Rashwani, Omran Saad, Fatima Al Zahraa Zaarour, Mohamad HajjHassan, Mohamad Abou Ali, L. Hamawy, A. Kassem
Ursino is one of the exceptional authors and researchers who described distinct regulatory mechanisms which control the haemodynamic variables during hypoxia. Obstacles we faced with Ursino are several incomplete implementations of mathematical models, which necessitate combining more than one of his researches. Combining partitions of such researches using different software (MATLAB/SIMULINK) other than what Ursino did use (SIMNON), grant the lead to more polished performance. SIMULINK software is much faster, easier to use, outputs more accurate and fine-tuned signals, with the ability to analyze any output at real-time simulation. Moreover, the implementation of Ursino’s work lacks controlling the overall system, which can be settled using Model Predictive Controller (MPC). This latter is a Multi-Input/Multi-Output (MIMO) controller that carries several outputs of the implemented model and referenced data, giving birth to numerous signals as stimuli for plant-parts of the system. Results show how MPC controller is ruling the thresholds of the sympathetic efferent activities to the heart and vessels, driving them to regulate arterial pressure of oxygen (PaO2) in blood to its initial normal range.
{"title":"MATLAB Modeling of Cardiovascular Response to Hypoxia with Control","authors":"Kinana Rashwani, Omran Saad, Fatima Al Zahraa Zaarour, Mohamad HajjHassan, Mohamad Abou Ali, L. Hamawy, A. Kassem","doi":"10.1109/ICABME53305.2021.9604829","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604829","url":null,"abstract":"Ursino is one of the exceptional authors and researchers who described distinct regulatory mechanisms which control the haemodynamic variables during hypoxia. Obstacles we faced with Ursino are several incomplete implementations of mathematical models, which necessitate combining more than one of his researches. Combining partitions of such researches using different software (MATLAB/SIMULINK) other than what Ursino did use (SIMNON), grant the lead to more polished performance. SIMULINK software is much faster, easier to use, outputs more accurate and fine-tuned signals, with the ability to analyze any output at real-time simulation. Moreover, the implementation of Ursino’s work lacks controlling the overall system, which can be settled using Model Predictive Controller (MPC). This latter is a Multi-Input/Multi-Output (MIMO) controller that carries several outputs of the implemented model and referenced data, giving birth to numerous signals as stimuli for plant-parts of the system. Results show how MPC controller is ruling the thresholds of the sympathetic efferent activities to the heart and vessels, driving them to regulate arterial pressure of oxygen (PaO2) in blood to its initial normal range.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"38 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":"131540921","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}
Pub Date : 2021-10-07DOI: 10.1109/ICABME53305.2021.9604897
Walid Kamali, Bassel El Osta, Bassem Hmouda, M. Fawal
Foot drop is a condition in which the dorsiflexor muscles of the foot and ankle are paralyzed or weak, causing the foot and toes to drag. It can occur because of trauma, arthroplasty surgery, neurophysiological deficits, or tumors. The research goal was to develop an endo-prosthesis that would allow patients with foot drop disease to regain nearly normal biomechanical motion. We designed a bio-mechanical endo-prosthesis from stainless steel 316 using real patient forces as well as determining the average weight of the foot in both sexes. The dimensions of the endo-prosthesis have been estimated using software. The concept was granted a patent in the United States, and a survey was issued to physicians and patients to gather feedback. However, the yield strength device simulation revealed an endless extension, and it is modest, measuring roughly 5 cm in length and 2 cm in diameter. Taking into consideration that the device is very simple and has tremendous potential and it will be an ideal treatment in the future for foot drop comparing to the well-known alternative treatments.
{"title":"Foot Drop Inventory Management (FDIM)","authors":"Walid Kamali, Bassel El Osta, Bassem Hmouda, M. Fawal","doi":"10.1109/ICABME53305.2021.9604897","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604897","url":null,"abstract":"Foot drop is a condition in which the dorsiflexor muscles of the foot and ankle are paralyzed or weak, causing the foot and toes to drag. It can occur because of trauma, arthroplasty surgery, neurophysiological deficits, or tumors. The research goal was to develop an endo-prosthesis that would allow patients with foot drop disease to regain nearly normal biomechanical motion. We designed a bio-mechanical endo-prosthesis from stainless steel 316 using real patient forces as well as determining the average weight of the foot in both sexes. The dimensions of the endo-prosthesis have been estimated using software. The concept was granted a patent in the United States, and a survey was issued to physicians and patients to gather feedback. However, the yield strength device simulation revealed an endless extension, and it is modest, measuring roughly 5 cm in length and 2 cm in diameter. Taking into consideration that the device is very simple and has tremendous potential and it will be an ideal treatment in the future for foot drop comparing to the well-known alternative treatments.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"40 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":"133303220","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/ICABME53305.2021.9604870
Nadia Abu Farha, Fares Al-Shargie, U. Tariq, H. Al-Nashash
In this paper, we present a preprocessing pipeline of Eye tracking data to assess cognitive vigilance levels. We introduced two different levels of vigilance state; alertness and vigilance decrement while subjects were performing Stroop Color-Word Task (SCWT) for approximately 45 minutes. We assessed the levels of vigilance by utilizing Eye tracking data and five machine learning (ML) classifiers. Our preprocessing pipeline consists of baseline correction, and artifacts, and noise removal. We extracted six features namely: fixation duration, pupil size, saccade duration, saccade amplitude, saccade velocity, and blink duration. These features were then used as an input to the five ML classifiers for vigilance level classification. We achieved the highest classification accuracy of 76.8% in differentiating between the two vigilance levels using all features with a selected Support vector machine classifier. Other classifiers have also achieved comparable accuracy.
{"title":"Artifact Removal of Eye Tracking Data for the Assessment of Cognitive Vigilance Levels","authors":"Nadia Abu Farha, Fares Al-Shargie, U. Tariq, H. Al-Nashash","doi":"10.1109/ICABME53305.2021.9604870","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604870","url":null,"abstract":"In this paper, we present a preprocessing pipeline of Eye tracking data to assess cognitive vigilance levels. We introduced two different levels of vigilance state; alertness and vigilance decrement while subjects were performing Stroop Color-Word Task (SCWT) for approximately 45 minutes. We assessed the levels of vigilance by utilizing Eye tracking data and five machine learning (ML) classifiers. Our preprocessing pipeline consists of baseline correction, and artifacts, and noise removal. We extracted six features namely: fixation duration, pupil size, saccade duration, saccade amplitude, saccade velocity, and blink duration. These features were then used as an input to the five ML classifiers for vigilance level classification. We achieved the highest classification accuracy of 76.8% in differentiating between the two vigilance levels using all features with a selected Support vector machine classifier. Other classifiers have also achieved comparable accuracy.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"106 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":"117083263","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/ICABME53305.2021.9604872
Hatem Tarhini, Rayan Mohamad, Abbas Rammal, M. Ayache
In the light of the rapidly growing COVID-19 pandemic, the need for an expeditious diagnosis of COVID-19 infection became essential. The immediate diagnosis will allow the initiation of the isolation process and adequate treatment as well. While the standard test used for the diagnosis of COVID-19 disease (RT-PCR) is usually time consuming (6 hours up to days in some centers); the need for a highly sensitive test became essential. Many studies have illustrated the utility of chest CT scan in the diagnoses of COVID-19. This paper evaluates the value of classical machine learning techniques and the convolutional neural networks in aiding physicians to further classify patients into either COVID-19 positive or negative according to their chest CT findings, and thus facilitating their work. To address this problem, this paper proposes classical neural networks using statistical features and deep CNN models to further classify a dataset of preprocessed chest CT images, using several classifiers and to evaluate the results. This latter showed that the best proposed method was a four layers CNN with SVM classifier with 99.6% accuracy. This demonstrates the potential of the proposed technique in computer-aided diagnosis for healthcare applications, especially for COVID-19 classification.
{"title":"Lung Segmentation followed by Machine Learning & Deep Learning Techniques for COVID-19 Detection in lung CT Images","authors":"Hatem Tarhini, Rayan Mohamad, Abbas Rammal, M. Ayache","doi":"10.1109/ICABME53305.2021.9604872","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604872","url":null,"abstract":"In the light of the rapidly growing COVID-19 pandemic, the need for an expeditious diagnosis of COVID-19 infection became essential. The immediate diagnosis will allow the initiation of the isolation process and adequate treatment as well. While the standard test used for the diagnosis of COVID-19 disease (RT-PCR) is usually time consuming (6 hours up to days in some centers); the need for a highly sensitive test became essential. Many studies have illustrated the utility of chest CT scan in the diagnoses of COVID-19. This paper evaluates the value of classical machine learning techniques and the convolutional neural networks in aiding physicians to further classify patients into either COVID-19 positive or negative according to their chest CT findings, and thus facilitating their work. To address this problem, this paper proposes classical neural networks using statistical features and deep CNN models to further classify a dataset of preprocessed chest CT images, using several classifiers and to evaluate the results. This latter showed that the best proposed method was a four layers CNN with SVM classifier with 99.6% accuracy. This demonstrates the potential of the proposed technique in computer-aided diagnosis for healthcare applications, especially for COVID-19 classification.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"134 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":"123458562","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/ICABME53305.2021.9604833
S. Allouch, Mahmoud Hassan, M. Yochum, Joan Duprez, M. Khalil, F. Wendling, J. Modolo, A. Kabbara
In the past years, the emergent method called "electroencephalography (EEG) source connectivity" has gained increased interest due to its ability to identify large-scale brain networks with satisfactory spatio-temporal resolution. However, many related methodological questions remain unanswered and no consensus has been reached yet over a unified EEG source connectivity pipeline. The objective evaluation of the pipeline is challenged by the absence of a ground truth when dealing with real EEG data. In this paper, we show how a recently developed, large-scale, physiologically-grounded computational model, named COALIA, can provide such "ground-truth" models by generating cortical and scalp-level realistic simulations of brain activity. We investigated the effect of three factors involved in the "EEG source connectivity" pipeline: the number of EEG sensors, the solution of the inverse problem, and the functional connectivity measure, in the context of epileptiform activity. Results showed that increasing the number of electrodes (at least channels) leads to a higher accuracy of the reconstructed cortical networks, and that the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI) has the best performance at high electrode density. Although we believe that these results are context-specific, the model-based approach presented in this paper can be extended to address other methodological aspects of the EEG source connectivity pipeline in different contexts. We aim at presenting a proof-of-concept of the potential use of COALIA in the optimization the EEG source connectivity pipeline.
{"title":"COALIA: a ground-truth for the evaluation of the EEG source connectivity","authors":"S. Allouch, Mahmoud Hassan, M. Yochum, Joan Duprez, M. Khalil, F. Wendling, J. Modolo, A. Kabbara","doi":"10.1109/ICABME53305.2021.9604833","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604833","url":null,"abstract":"In the past years, the emergent method called \"electroencephalography (EEG) source connectivity\" has gained increased interest due to its ability to identify large-scale brain networks with satisfactory spatio-temporal resolution. However, many related methodological questions remain unanswered and no consensus has been reached yet over a unified EEG source connectivity pipeline. The objective evaluation of the pipeline is challenged by the absence of a ground truth when dealing with real EEG data. In this paper, we show how a recently developed, large-scale, physiologically-grounded computational model, named COALIA, can provide such \"ground-truth\" models by generating cortical and scalp-level realistic simulations of brain activity. We investigated the effect of three factors involved in the \"EEG source connectivity\" pipeline: the number of EEG sensors, the solution of the inverse problem, and the functional connectivity measure, in the context of epileptiform activity. Results showed that increasing the number of electrodes (at least channels) leads to a higher accuracy of the reconstructed cortical networks, and that the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI) has the best performance at high electrode density. Although we believe that these results are context-specific, the model-based approach presented in this paper can be extended to address other methodological aspects of the EEG source connectivity pipeline in different contexts. We aim at presenting a proof-of-concept of the potential use of COALIA in the optimization the EEG source connectivity pipeline.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"26 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":"125940305","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/ICABME53305.2021.9604902
Alaa Daher, Sally Yassin, M. Ayache
This paper presents a new concept for electrocardiogram (ECG) signals compression based on Fourier series modeling. The goal of the compression is to enable the ECG Holter to record and store ECG data for several days instead of just 24 hours while maintaining all the features of the signals. This proposed method can be used to record up to 26 days when using Fourier series of 4th degree, and 21 days when using Fourier series of 5th degree, whith high accuracy and a mean square error (RMSE) of approximately 0.001, which is considered extremely low and satisfactory.
{"title":"A Novel ECG Waves Detection Followed by a New Compression Technique Based on Fourier Series Modeling for up to 26 Days Holter Monitor","authors":"Alaa Daher, Sally Yassin, M. Ayache","doi":"10.1109/ICABME53305.2021.9604902","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604902","url":null,"abstract":"This paper presents a new concept for electrocardiogram (ECG) signals compression based on Fourier series modeling. The goal of the compression is to enable the ECG Holter to record and store ECG data for several days instead of just 24 hours while maintaining all the features of the signals. This proposed method can be used to record up to 26 days when using Fourier series of 4th degree, and 21 days when using Fourier series of 5th degree, whith high accuracy and a mean square error (RMSE) of approximately 0.001, which is considered extremely low and satisfactory.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"36 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":"126897209","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/ICABME53305.2021.9604860
Yasser Saad, Ali Mustapha, Ali Cherry
Coronavirus sickness (COVID-19) may be a pandemic sickness, that has already caused thousands of casualties and infected many countless individuals worldwide. Whereas most of the individuals infected with the COVID-19 intimate with delicate to moderate respiratory disease, some developed deadly respiratory illness. Any technological tool sanctioning screening of the COVID-19 infection with high accuracy will be crucially useful to the attention professionals. The usage of chest CT scan pictures for classifying and diagnosing COVID-19 respiratory illness has shown an excellent range of exactness and accuracy quite the other tool that lessens the number of deaths within the severe cases. This paper presents a proposed model of convolutional neural network (CNN) with a large multi-national dataset that is able to classify covid-19 pneumonia; lung cancer and the normal lung tissues from chest computed tomography (CT) scans with a classification accuracy of 94.05%.
{"title":"Automatic classification between COVID-19 pneumonia, lung cancer and normal lung tissues on chest CT Scans","authors":"Yasser Saad, Ali Mustapha, Ali Cherry","doi":"10.1109/ICABME53305.2021.9604860","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604860","url":null,"abstract":"Coronavirus sickness (COVID-19) may be a pandemic sickness, that has already caused thousands of casualties and infected many countless individuals worldwide. Whereas most of the individuals infected with the COVID-19 intimate with delicate to moderate respiratory disease, some developed deadly respiratory illness. Any technological tool sanctioning screening of the COVID-19 infection with high accuracy will be crucially useful to the attention professionals. The usage of chest CT scan pictures for classifying and diagnosing COVID-19 respiratory illness has shown an excellent range of exactness and accuracy quite the other tool that lessens the number of deaths within the severe cases. This paper presents a proposed model of convolutional neural network (CNN) with a large multi-national dataset that is able to classify covid-19 pneumonia; lung cancer and the normal lung tissues from chest computed tomography (CT) scans with a classification accuracy of 94.05%.","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":"129250898","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/ICABME53305.2021.9604881
R. Tout, Alaa Daher
This study aims to reveal the electromyography and spirometry relationship of the respiratory cycle, and to establish the chronology of the contraction between the Scalene and the Rectus abdominis. Purpose: Expose the electromyography and spirometry relationship and establish the chronology of the contraction of Scalene and Rectus abdominis which works together in synergy antagonism in physiological breathing.In our study 128 electromyographic tests were performed during the respiratory cycle on 43 healthy adults. EMG signals of Scalene, Rectus abdominis were recorded. The breathing was recorded by using a spirometer (vernier®).The obtained results showed that the duration of the contraction of Scalene is superior to Rectus abdominis 82% p-value=0.000058, the amplitude of Scalene is superior to Rectus abdominis, p-value=0.000000073. 109 tests of Scalene contraction begin before that of Rectus abdominis (63.74%), p-value=0.000012. RMS is 0.02 ± 0.011 μv for Rectus abdominis and 0.04 ± 0.021 μv for Scalene, p-value=6.76591E-06. The duration of inspiration is 1.25 s ± 0.19, the expiration is 1.04 s ± 0.19. The mean frequency of Rectus abdominis is 54.19 Hz ± 6.35, it is 57.21 Hz ± 7.08 for Scalene, the p-value is 9.84081E-08. The median frequency of Rectus abdominis is 51.05 Hz ± 6.51, it is 52.72 Hz ± 6.94 for Scalene, the p-value is 0.0098. The muscle fatigue of the Rectus abdominis decreased from 60.40 ± 0.45 to 19.98 ± 4.32. For Scalene it decreased from 60.41 ± 0.4 to 23.52 ± 4.41.As Conclusion, there is a synergistic-antagonism relationship between Scalene and Rectus abdominis during respiration. Scalene is a main inspiratory muscle, its contraction is important in amplitude, duration, and frequency. Both muscles are fatigable during the inspiratory cycle.
{"title":"Synergy Relationship Between The Scalene And The Rectus Abdominis During The Respiratory Cycle In Healthy Subjects","authors":"R. Tout, Alaa Daher","doi":"10.1109/ICABME53305.2021.9604881","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604881","url":null,"abstract":"This study aims to reveal the electromyography and spirometry relationship of the respiratory cycle, and to establish the chronology of the contraction between the Scalene and the Rectus abdominis. Purpose: Expose the electromyography and spirometry relationship and establish the chronology of the contraction of Scalene and Rectus abdominis which works together in synergy antagonism in physiological breathing.In our study 128 electromyographic tests were performed during the respiratory cycle on 43 healthy adults. EMG signals of Scalene, Rectus abdominis were recorded. The breathing was recorded by using a spirometer (vernier®).The obtained results showed that the duration of the contraction of Scalene is superior to Rectus abdominis 82% p-value=0.000058, the amplitude of Scalene is superior to Rectus abdominis, p-value=0.000000073. 109 tests of Scalene contraction begin before that of Rectus abdominis (63.74%), p-value=0.000012. RMS is 0.02 ± 0.011 μv for Rectus abdominis and 0.04 ± 0.021 μv for Scalene, p-value=6.76591E-06. The duration of inspiration is 1.25 s ± 0.19, the expiration is 1.04 s ± 0.19. The mean frequency of Rectus abdominis is 54.19 Hz ± 6.35, it is 57.21 Hz ± 7.08 for Scalene, the p-value is 9.84081E-08. The median frequency of Rectus abdominis is 51.05 Hz ± 6.51, it is 52.72 Hz ± 6.94 for Scalene, the p-value is 0.0098. The muscle fatigue of the Rectus abdominis decreased from 60.40 ± 0.45 to 19.98 ± 4.32. For Scalene it decreased from 60.41 ± 0.4 to 23.52 ± 4.41.As Conclusion, there is a synergistic-antagonism relationship between Scalene and Rectus abdominis during respiration. Scalene is a main inspiratory muscle, its contraction is important in amplitude, duration, and frequency. Both muscles are fatigable during the inspiratory cycle.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"36 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":"125817651","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 : 2021-05-11DOI: 10.1109/ICABME53305.2021.9604861
S. E. Falou, F. Trad
Since the beginning of the COVID-19 epidemic, governments have been attempting to mitigate its impact on their citizens and countries, and the main way of doing this was through Non-Pharmaceutical Interventions (NPIs) that ranged from universal masking and social isolation to worldwide lockdowns. Given that the virus is still new, a government does not always know what to expect after applying a specific measure, but ideally, if countries knew beforehand the effect of their actions, they would always choose what works best for their citizens, and this is what we seek from our study. Our goal is to conceptualize a system that helps governments make the right decisions during a pandemic. For this purpose, we built a simulator to simulate the spread of COVID-19 in a virtual country – where we can apply different NPIs at different times – using an Agent-Based Model that runs on top of the Monte Carlo Algorithm. Our Simulator was first validated on concepts (e.g. Flattening the Curve and Second Wave scenario) to make sure it reflects realistic COVID-19 aspects. Then, it was used to simulate the case of Lebanon, and forecast the effect of opening schools and universities on the pandemic situation since the Lebanese Ministry of Education was planning to do so starting from 21 April 2021. Our validations prove that this prototype can be very beneficial for a country like Lebanon to carry a better decision making during the pandemic.
{"title":"Forecast Analysis of the COVID-19 Incidence in Lebanon: Prediction of Future Epidemiological Trends to Plan More Effective Control Programs","authors":"S. E. Falou, F. Trad","doi":"10.1109/ICABME53305.2021.9604861","DOIUrl":"https://doi.org/10.1109/ICABME53305.2021.9604861","url":null,"abstract":"Since the beginning of the COVID-19 epidemic, governments have been attempting to mitigate its impact on their citizens and countries, and the main way of doing this was through Non-Pharmaceutical Interventions (NPIs) that ranged from universal masking and social isolation to worldwide lockdowns. Given that the virus is still new, a government does not always know what to expect after applying a specific measure, but ideally, if countries knew beforehand the effect of their actions, they would always choose what works best for their citizens, and this is what we seek from our study. Our goal is to conceptualize a system that helps governments make the right decisions during a pandemic. For this purpose, we built a simulator to simulate the spread of COVID-19 in a virtual country – where we can apply different NPIs at different times – using an Agent-Based Model that runs on top of the Monte Carlo Algorithm. Our Simulator was first validated on concepts (e.g. Flattening the Curve and Second Wave scenario) to make sure it reflects realistic COVID-19 aspects. Then, it was used to simulate the case of Lebanon, and forecast the effect of opening schools and universities on the pandemic situation since the Lebanese Ministry of Education was planning to do so starting from 21 April 2021. Our validations prove that this prototype can be very beneficial for a country like Lebanon to carry a better decision making during the pandemic.","PeriodicalId":294393,"journal":{"name":"2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115936260","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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