Elhoussine Talab, Omar Mohamed, Labeeba Begum, F. Aloul, A. Sagahyroon
One out of four deaths is caused by heart related issues. Acting upon early signs of heart disease can, thus, drastically increase probability of saving lives. This paper discusses a cost-effective and reliable method of diagnosing heart abnormalities by using mobile phones that are nowadays typically available to an average user. A mobile application is developed to detect heart abnormal activities using either a digital stethoscope measurement as input, or a mobile recording of the heart beat using the mobile's microphone. To process the raw heart sound data, we first denoise the signal using wavelet transforms, and then apply machine learning techniques, namely, Convolutional Neural Networks for the classification of the stored heart sounds. A database consisting of recorded human heart sounds and their corresponding diagnosis is used to train the neural network. Moreover, neural network fine-tuning techniques such as ADAM Regularization is used to smoothen the prediction process. The proposed approach is tested on heart sound signals, that are 5 to 8 seconds long, and is shown to perform with an accuracy of 94.2% on the validation set.
{"title":"Detecting Heart Anomalies Using Mobile Phones and Machine Learning","authors":"Elhoussine Talab, Omar Mohamed, Labeeba Begum, F. Aloul, A. Sagahyroon","doi":"10.1109/BIBE.2019.00083","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00083","url":null,"abstract":"One out of four deaths is caused by heart related issues. Acting upon early signs of heart disease can, thus, drastically increase probability of saving lives. This paper discusses a cost-effective and reliable method of diagnosing heart abnormalities by using mobile phones that are nowadays typically available to an average user. A mobile application is developed to detect heart abnormal activities using either a digital stethoscope measurement as input, or a mobile recording of the heart beat using the mobile's microphone. To process the raw heart sound data, we first denoise the signal using wavelet transforms, and then apply machine learning techniques, namely, Convolutional Neural Networks for the classification of the stored heart sounds. A database consisting of recorded human heart sounds and their corresponding diagnosis is used to train the neural network. Moreover, neural network fine-tuning techniques such as ADAM Regularization is used to smoothen the prediction process. The proposed approach is tested on heart sound signals, that are 5 to 8 seconds long, and is shown to perform with an accuracy of 94.2% on the validation set.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133050380","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}
E. Mylona, Clement Lebreton, P. Fontaine, S. Supiot, N. Magné, G. Créhange, R. Crevoisier, O. Acosta
Prostate cancer radiotherapy unavoidably involves the irradiation not only of the target volume, but also of healthy organs-at-risk, neighboring the prostate, likely causing adverse, toxicity-related side-effects. Specifically, in the case of urinary toxicity, these side effects might be associated with a variety of dosimetric, clinical and genetic factors, making its prediction particularly challenging. Given the inconsistency of available data concerning radiation-induced toxicity, it is crucial to develop robust models with superior predictive performance in order to perform tailored treatments. Machine Learning techniques emerge as appealing in this context, nevertheless without any consensus on the best algorithms to be used. This work proposes a comparison of several machine-learning strategies together with different minority class oversampling techniques for prediction of urinary toxicity following prostate cancer radiotherapy using dosimetric and clinical data. The performance of these classifiers was evaluated on the original dataset and using four different synthetic oversampling techniques. The area under the ROC curve (AUC) and the F-measure were employed to evaluate their performance. Results suggest that, regardless of the technique, oversampling always increases the prediction performance of the models (p=0.004). Overall, oversampling with Synthetic Minority Oversampling Technique (SMOTE) followed by Edited Nearest Neighbour algorithm (ENN) together with Regularized Discriminant Analysis (RDA) classifier provide the best performance (AUC=0.71).
{"title":"Comparison of Machine Learning Algorithms and Oversampling Techniques for Urinary Toxicity Prediction After Prostate Cancer Radiotherapy","authors":"E. Mylona, Clement Lebreton, P. Fontaine, S. Supiot, N. Magné, G. Créhange, R. Crevoisier, O. Acosta","doi":"10.1109/BIBE.2019.00180","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00180","url":null,"abstract":"Prostate cancer radiotherapy unavoidably involves the irradiation not only of the target volume, but also of healthy organs-at-risk, neighboring the prostate, likely causing adverse, toxicity-related side-effects. Specifically, in the case of urinary toxicity, these side effects might be associated with a variety of dosimetric, clinical and genetic factors, making its prediction particularly challenging. Given the inconsistency of available data concerning radiation-induced toxicity, it is crucial to develop robust models with superior predictive performance in order to perform tailored treatments. Machine Learning techniques emerge as appealing in this context, nevertheless without any consensus on the best algorithms to be used. This work proposes a comparison of several machine-learning strategies together with different minority class oversampling techniques for prediction of urinary toxicity following prostate cancer radiotherapy using dosimetric and clinical data. The performance of these classifiers was evaluated on the original dataset and using four different synthetic oversampling techniques. The area under the ROC curve (AUC) and the F-measure were employed to evaluate their performance. Results suggest that, regardless of the technique, oversampling always increases the prediction performance of the models (p=0.004). Overall, oversampling with Synthetic Minority Oversampling Technique (SMOTE) followed by Edited Nearest Neighbour algorithm (ENN) together with Regularized Discriminant Analysis (RDA) classifier provide the best performance (AUC=0.71).","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"26 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130347046","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}
Nolwenn Jegou, Franck Desaize, Gobert N. Lee, M. Bajger, O. Acosta, J. Leseur, R. Crevoisier, Martin Caon
In radiotherapy, computed tomography (CT) images are typically used for radiation treatment planning. Accurate segmentation of radiation sensitive healthy tissues, organs-atrisk (OARs), is important for radiation treatment planning for brain tumor. 3D Slicer has been applied in many medical applications including tumor segmentation on head MR images. However, to the best of our knowledge, there have been no studies using 3D Slicer for segmenting OARs on head CT images. This preliminary study evaluates the segmentation of seven OARs on head CTs using 3D Slicer. Results are comparable to state-ofthe- art approaches but a larger dataset is required to verify the results.
{"title":"Organs-at-Risk Contouring on Head CT for RT Planning Using 3D Slicer– A Preliminary Study","authors":"Nolwenn Jegou, Franck Desaize, Gobert N. Lee, M. Bajger, O. Acosta, J. Leseur, R. Crevoisier, Martin Caon","doi":"10.1109/BIBE.2019.00097","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00097","url":null,"abstract":"In radiotherapy, computed tomography (CT) images are typically used for radiation treatment planning. Accurate segmentation of radiation sensitive healthy tissues, organs-atrisk (OARs), is important for radiation treatment planning for brain tumor. 3D Slicer has been applied in many medical applications including tumor segmentation on head MR images. However, to the best of our knowledge, there have been no studies using 3D Slicer for segmenting OARs on head CT images. This preliminary study evaluates the segmentation of seven OARs on head CTs using 3D Slicer. Results are comparable to state-ofthe- art approaches but a larger dataset is required to verify the results.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115706416","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}
Georgios S. Ioannidis, K. Nikiforaki, A. Karantanas
The aim of the present work is to correlate perfusion information obtained from semi-quantitative DCE data analysis with quantitative diffusion data analysis in patients with peripheral arterial disease. An in-house built software deploying linear and nonlinear least squares algorithms, was used for the quantification of the parameters based on intra-voxel incoherent motion (IVIM) model and exponentially modified Gaussian function. All numerical calculations were implemented in Python 3.5. Derived per-fusion parameters (micro-perfusion fraction f and Wash-In respectively) showed good correlation (>0.5). This constitutes a promising result for obtaining perfusion information from DWI sequences without the need for contrast agent in patients with vascular disease.
{"title":"Correlation of DWI and DCE MRI Markers for the Study of Perfusion of the Lower Limb in Patients with Peripheral Arterial Disease","authors":"Georgios S. Ioannidis, K. Nikiforaki, A. Karantanas","doi":"10.1109/BIBE.2019.00084","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00084","url":null,"abstract":"The aim of the present work is to correlate perfusion information obtained from semi-quantitative DCE data analysis with quantitative diffusion data analysis in patients with peripheral arterial disease. An in-house built software deploying linear and nonlinear least squares algorithms, was used for the quantification of the parameters based on intra-voxel incoherent motion (IVIM) model and exponentially modified Gaussian function. All numerical calculations were implemented in Python 3.5. Derived per-fusion parameters (micro-perfusion fraction f and Wash-In respectively) showed good correlation (>0.5). This constitutes a promising result for obtaining perfusion information from DWI sequences without the need for contrast agent in patients with vascular disease.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115912365","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}
Dimitrios Pleouras, A. Sakellarios, G. Karanasiou, S. Kyriakidis, Panagiota I. Tsompou, Vassiliki I. Kigka, D. Fotiadis
Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for its treatment. This study is aiming to investigate the role of diabetes in the atherosclerotic plaque growth mechanisms through the utilization of a multi-level numerical model. To accomplish this, we developed a proof-of-concept mathematical model of the diabetes effect to plaque growth, that has been coupled to a stateof-the-art multi-level numerical model of plaque growth. Diabetes main effect is the increase of the average blood glucose concentration, which causes the decrease of the endothelial nitric oxide production rate by affecting several biologic pathways. Nitric oxide is a signaling molecule that regulates the endothelial flow rates, and any abnormal alteration leads to endothelial dysfunction, the major culprit of atherosclerosis. The derived model considers the modeling of blood flow in lumen and of species transport and reactions in the arterial wall. The considered factors include: (i) LDL, (ii) HDL, (iii) oxidized LDL, (iv) monocytes, (v) macrophages, (vi) cytokines, (vii) smooth muscle cells (contractile & synthetic), and (viii) collagen. The model is validated using 10 patients' reconstructed arterial data in two time-points. More specifically, baseline geometries are used as an input to our model, while follow-up geometries are used as benchmark for our model's output. The results presented a high coefficient of determination between the simulated with diabetes effect and the real follow-up geometries of 0.634.
动脉粥样硬化是世界范围内死亡的主要原因之一，迫切需要对其进行治疗。本研究旨在通过多层次数值模型探讨糖尿病在动脉粥样硬化斑块生长机制中的作用。为了实现这一目标，我们开发了糖尿病对斑块生长影响的概念验证数学模型，该模型已与最先进的斑块生长多层次数值模型相结合。糖尿病的主要作用是平均血糖浓度升高，通过影响几种生物途径引起内皮细胞一氧化氮生成速率降低。一氧化氮是调节内皮血流速率的信号分子，任何异常改变都会导致内皮功能障碍，这是动脉粥样硬化的罪魁祸首。导出的模型考虑了管腔内血流和动脉壁内物质运输和反应的建模。考虑的因素包括:(i) LDL， (ii) HDL， (iii)氧化LDL， (iv)单核细胞，(v)巨噬细胞，(vi)细胞因子，(vii)平滑肌细胞(收缩和合成)，(viii)胶原蛋白。利用10例患者在两个时间点的重建动脉数据对模型进行了验证。更具体地说，基线几何被用作模型的输入，而后续几何被用作模型输出的基准。结果表明，模拟的糖尿病效应与实际随访几何值之间的决定系数为0.634。
{"title":"Atherosclerotic Plaque Growth Prediction in Coronary Arteries using a Computational Multi-level Model: The Effect of Diabetes","authors":"Dimitrios Pleouras, A. Sakellarios, G. Karanasiou, S. Kyriakidis, Panagiota I. Tsompou, Vassiliki I. Kigka, D. Fotiadis","doi":"10.1109/BIBE.2019.00132","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00132","url":null,"abstract":"Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for its treatment. This study is aiming to investigate the role of diabetes in the atherosclerotic plaque growth mechanisms through the utilization of a multi-level numerical model. To accomplish this, we developed a proof-of-concept mathematical model of the diabetes effect to plaque growth, that has been coupled to a stateof-the-art multi-level numerical model of plaque growth. Diabetes main effect is the increase of the average blood glucose concentration, which causes the decrease of the endothelial nitric oxide production rate by affecting several biologic pathways. Nitric oxide is a signaling molecule that regulates the endothelial flow rates, and any abnormal alteration leads to endothelial dysfunction, the major culprit of atherosclerosis. The derived model considers the modeling of blood flow in lumen and of species transport and reactions in the arterial wall. The considered factors include: (i) LDL, (ii) HDL, (iii) oxidized LDL, (iv) monocytes, (v) macrophages, (vi) cytokines, (vii) smooth muscle cells (contractile & synthetic), and (viii) collagen. The model is validated using 10 patients' reconstructed arterial data in two time-points. More specifically, baseline geometries are used as an input to our model, while follow-up geometries are used as benchmark for our model's output. The results presented a high coefficient of determination between the simulated with diabetes effect and the real follow-up geometries of 0.634.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116164565","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}
Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleeprelated disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring process. EEG recordings data are acquired from three different groups comprising of healthy subjects and people with minor sleep disorders. A mixture of time domain and frequency domain features are extracted. Temporal feature changes are utilized in order to capture contextual information of the EEG signal. Multiple classifiers are tested, culminating in a voting classifier, achieving a maximum accuracy of 90.8% for the healthy subjects' group. The main novelty introduced by the proposed solution is the algorithm's high accuracy when tested on a mixed dataset of healthy and patient subjects. The promising capabilities that derive from the successful implementation of this solution are discussed in the conclusions.
{"title":"Classification of Sleep Stages for Healthy Subjects and Patients with Minor Sleep Disorders","authors":"C. Timplalexis, K. Diamantaras, I. Chouvarda","doi":"10.1109/BIBE.2019.00068","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00068","url":null,"abstract":"Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleeprelated disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring process. EEG recordings data are acquired from three different groups comprising of healthy subjects and people with minor sleep disorders. A mixture of time domain and frequency domain features are extracted. Temporal feature changes are utilized in order to capture contextual information of the EEG signal. Multiple classifiers are tested, culminating in a voting classifier, achieving a maximum accuracy of 90.8% for the healthy subjects' group. The main novelty introduced by the proposed solution is the algorithm's high accuracy when tested on a mixed dataset of healthy and patient subjects. The promising capabilities that derive from the successful implementation of this solution are discussed in the conclusions.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123046834","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}
M. Strauch, Karl Krüger, L. Mukunda, Alja Lüdke, C. Galizia, D. Merhof
The odorant receptor neurons on the fruit fly antenna are highly sensitive to a broad range of chemicals. A compound signal of receptor activity on the antenna can be read out in real time with functional neuroimaging, and individual receptor responses to hundreds of odorants are available in a database. Utilizing the fruit fly antenna as chemosensor enables applications ranging from biomarker detection to identification of unknown chemicals in samples. Here, we propose to connect neural response spaces, mapping odorant responses from one fly to another and to database space. A map is defined exactly for reference odorants common to both subject and target space, while the map for the remaining odorants is estimated based on radial basis function interpolation. On a data set with chemically diverse odorants, mapping to another antenna allows identifying unlabelled subject space odorants by the proximity of their mapped position to labelled odorants in target space. Furthermore, mapping from antenna to database space predicts the individual receptor responses significantly better than a random baseline model, suggesting that receptor responses can be inferred from the compound antenna signal given a sufficiently dense net of reference odorants to support the map.
{"title":"Interpolating Maps between Neural Response Spaces for Chemosensing with Fruit Fly Antenna Sensors","authors":"M. Strauch, Karl Krüger, L. Mukunda, Alja Lüdke, C. Galizia, D. Merhof","doi":"10.1109/BIBE.2019.00135","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00135","url":null,"abstract":"The odorant receptor neurons on the fruit fly antenna are highly sensitive to a broad range of chemicals. A compound signal of receptor activity on the antenna can be read out in real time with functional neuroimaging, and individual receptor responses to hundreds of odorants are available in a database. Utilizing the fruit fly antenna as chemosensor enables applications ranging from biomarker detection to identification of unknown chemicals in samples. Here, we propose to connect neural response spaces, mapping odorant responses from one fly to another and to database space. A map is defined exactly for reference odorants common to both subject and target space, while the map for the remaining odorants is estimated based on radial basis function interpolation. On a data set with chemically diverse odorants, mapping to another antenna allows identifying unlabelled subject space odorants by the proximity of their mapped position to labelled odorants in target space. Furthermore, mapping from antenna to database space predicts the individual receptor responses significantly better than a random baseline model, suggesting that receptor responses can be inferred from the compound antenna signal given a sufficiently dense net of reference odorants to support the map.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125181346","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}
Gonul Gunal Degirmendereli, Sharlene D. Newman, F. Yarman-Vural
In this paper, we aim to measure the information content of brain anatomic regions using the functional magnetic resonance images (fMRI) recorded during a complex problem solving (CPS) task. We, also, analyze the brain regions, activated in different phases of the problem solving process. Previous studies have widely used machine learning approaches to examine the active anatomic regions for cognitive states of human subjects based on their fMRI data. This study proposes an information theoretic method for analyzing the activity in anatomic regions. Briefly, we define and estimate two types of Shannon entropy, namely, static and dynamic entropy, to understand how complex problem solving processes lead to changes in information content of anatomic regions. We investigate the relationship between the problem-solving task phases and the Shannon entropy measures suggested in this study, for the underlying brain activity during a Tower of London (TOL) problem solving process. We observe that the dynamic entropy fluctuations in brain regions during the CPS task provides a measure for the information content of the main phases of complex problem solving, namely planning and execution. We, also, observe that static entropy measures of anatomic regions are consistent with the experimental findings of neuroscience. The preliminary results show strong promise in using the suggested static and dynamic entropy as a measure for characterizing the brain states related to the problem solving process. This capability would be useful in revealing the hidden cognitive states of subjects performing a specific cognitive task.
{"title":"On the Entropy of Brain Anatomic Regions for Complex Problem Solving","authors":"Gonul Gunal Degirmendereli, Sharlene D. Newman, F. Yarman-Vural","doi":"10.1109/BIBE.2019.00115","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00115","url":null,"abstract":"In this paper, we aim to measure the information content of brain anatomic regions using the functional magnetic resonance images (fMRI) recorded during a complex problem solving (CPS) task. We, also, analyze the brain regions, activated in different phases of the problem solving process. Previous studies have widely used machine learning approaches to examine the active anatomic regions for cognitive states of human subjects based on their fMRI data. This study proposes an information theoretic method for analyzing the activity in anatomic regions. Briefly, we define and estimate two types of Shannon entropy, namely, static and dynamic entropy, to understand how complex problem solving processes lead to changes in information content of anatomic regions. We investigate the relationship between the problem-solving task phases and the Shannon entropy measures suggested in this study, for the underlying brain activity during a Tower of London (TOL) problem solving process. We observe that the dynamic entropy fluctuations in brain regions during the CPS task provides a measure for the information content of the main phases of complex problem solving, namely planning and execution. We, also, observe that static entropy measures of anatomic regions are consistent with the experimental findings of neuroscience. The preliminary results show strong promise in using the suggested static and dynamic entropy as a measure for characterizing the brain states related to the problem solving process. This capability would be useful in revealing the hidden cognitive states of subjects performing a specific cognitive task.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131306315","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}
B. Mathur, A. Topiwala, Saul Schaffer, M. Kam, H. Saeidi, T. Fleiter, A. Krieger
Trauma is among the leading causes of death in the United States with up to 29% of pre-hospital trauma deaths attributed to uncontrolled hemorrhages. This paper reports a semi-autonomous robotic system capable of assessing trauma using 2D and 3D image analysis and enabling remote focused assessment with sonography for trauma (FAST) en route to the hospital for earlier trauma diagnosis and faster initialization of life saving care. The system was able to accurately calculate FAST scan positions of patient specific phantoms using the measured phantom sizes and positions of the umbilicus. The system was capable of accurately classifying and localizing wounds, so they can be avoided during the ultrasound scan. These objects were localized with an accuracy of 0.94 ± 0.179cm and FAST exam locations were estimated with an accuracy of 2.2 ± 1.88cm. A radiologist successfully completed a remote FAST scan of the phantom using the system with improved image quality over manual scans, demonstrating feasibility of the system.
{"title":"A Semi-Autonomous Robotic System for Remote Trauma Assessment","authors":"B. Mathur, A. Topiwala, Saul Schaffer, M. Kam, H. Saeidi, T. Fleiter, A. Krieger","doi":"10.1109/BIBE.2019.00122","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00122","url":null,"abstract":"Trauma is among the leading causes of death in the United States with up to 29% of pre-hospital trauma deaths attributed to uncontrolled hemorrhages. This paper reports a semi-autonomous robotic system capable of assessing trauma using 2D and 3D image analysis and enabling remote focused assessment with sonography for trauma (FAST) en route to the hospital for earlier trauma diagnosis and faster initialization of life saving care. The system was able to accurately calculate FAST scan positions of patient specific phantoms using the measured phantom sizes and positions of the umbilicus. The system was capable of accurately classifying and localizing wounds, so they can be avoided during the ultrasound scan. These objects were localized with an accuracy of 0.94 ± 0.179cm and FAST exam locations were estimated with an accuracy of 2.2 ± 1.88cm. A radiologist successfully completed a remote FAST scan of the phantom using the system with improved image quality over manual scans, demonstrating feasibility of the system.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126951648","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}
Oscar L. Mosquera, D. Guzman, Jhon Zamudio, J. García, Cristhian Rodriguez, Daniel Botero
considering the strategic direction of the Colombian National Army, the need to increase training effectiveness using technological developments in biomedical engineering is highlighted. This study evaluates brain electrical activity via complex networks in virtual reality situations which simulate military reactions. Results suggest that a high network degree may be related to an appropriate decision-making process, whereas a lower value may be associated with poor performances according to military doctrine. While not entirely significant, some difference is appreciated, mainly between the base period and the event related to subject elimination (p=0.058). The authors also noted the burst suppression pattern when the subject was eliminated. As this is a work in progress, more research subjects are being recruited and more complex networks descriptors are being explored.
{"title":"Complex Brain Networks and Simulated Military Reactions using a Virtual Reality System","authors":"Oscar L. Mosquera, D. Guzman, Jhon Zamudio, J. García, Cristhian Rodriguez, Daniel Botero","doi":"10.1109/BIBE.2019.00105","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00105","url":null,"abstract":"considering the strategic direction of the Colombian National Army, the need to increase training effectiveness using technological developments in biomedical engineering is highlighted. This study evaluates brain electrical activity via complex networks in virtual reality situations which simulate military reactions. Results suggest that a high network degree may be related to an appropriate decision-making process, whereas a lower value may be associated with poor performances according to military doctrine. While not entirely significant, some difference is appreciated, mainly between the base period and the event related to subject elimination (p=0.058). The authors also noted the burst suppression pattern when the subject was eliminated. As this is a work in progress, more research subjects are being recruited and more complex networks descriptors are being explored.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115968741","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：