2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第4页

Detecting Heart Anomalies Using Mobile Phones and Machine Learning 利用手机和机器学习检测心脏异常

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00083

Elhoussine Talab, Omar Mohamed, Labeeba Begum, F. Aloul, A. Sagahyroon

{"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}

引用次数: 1

Comparison of Machine Learning Algorithms and Oversampling Techniques for Urinary Toxicity Prediction After Prostate Cancer Radiotherapy 机器学习算法与过采样技术在前列腺癌放疗后尿毒性预测中的比较

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00180

E. Mylona, Clement Lebreton, P. Fontaine, S. Supiot, N. Magné, G. Créhange, R. Crevoisier, O. Acosta

{"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}

引用次数: 3

Organs-at-Risk Contouring on Head CT for RT Planning Using 3D Slicer– A Preliminary Study 利用三维切片机对头部CT高危器官轮廓进行RT计划的初步研究

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00097

Nolwenn Jegou, Franck Desaize, Gobert N. Lee, M. Bajger, O. Acosta, J. Leseur, R. Crevoisier, Martin Caon

引用次数: 3

Correlation of DWI and DCE MRI Markers for the Study of Perfusion of the Lower Limb in Patients with Peripheral Arterial Disease DWI与DCE MRI标志物与外周动脉病变患者下肢血流灌注的相关性研究

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00084

Georgios S. Ioannidis, K. Nikiforaki, A. Karantanas

引用次数: 1

Atherosclerotic Plaque Growth Prediction in Coronary Arteries using a Computational Multi-level Model: The Effect of Diabetes 应用多层次计算模型预测冠状动脉粥样硬化斑块生长:糖尿病的影响

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00132

Dimitrios Pleouras, A. Sakellarios, G. Karanasiou, S. Kyriakidis, Panagiota I. Tsompou, Vassiliki I. Kigka, D. Fotiadis

{"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}

引用次数: 3

Classification of Sleep Stages for Healthy Subjects and Patients with Minor Sleep Disorders 健康受试者与轻度睡眠障碍患者的睡眠阶段分类

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00068

C. Timplalexis, K. Diamantaras, I. Chouvarda

引用次数: 9

Interpolating Maps between Neural Response Spaces for Chemosensing with Fruit Fly Antenna Sensors 果蝇天线传感器化学传感神经响应空间间的插值映射

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00135

M. Strauch, Karl Krüger, L. Mukunda, Alja Lüdke, C. Galizia, D. Merhof

{"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}

引用次数: 0

On the Entropy of Brain Anatomic Regions for Complex Problem Solving 用于复杂问题求解的脑解剖区域熵

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00115

Gonul Gunal Degirmendereli, Sharlene D. Newman, F. Yarman-Vural

{"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}

引用次数: 2

A Semi-Autonomous Robotic System for Remote Trauma Assessment 用于远程创伤评估的半自主机器人系统

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00122

B. Mathur, A. Topiwala, Saul Schaffer, M. Kam, H. Saeidi, T. Fleiter, A. Krieger

引用次数: 8

Complex Brain Networks and Simulated Military Reactions using a Virtual Reality System 使用虚拟现实系统的复杂大脑网络和模拟军事反应

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2019-10-01 DOI: 10.1109/BIBE.2019.00105

Oscar L. Mosquera, D. Guzman, Jhon Zamudio, J. García, Cristhian Rodriguez, Daniel Botero

引用次数: 1