Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference最新文献

Rapid Label-free DNA Quantification by Multi-frequency Impedance Sensing on a Chip. 芯片上多频阻抗传感快速无标记DNA定量。

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2019-07-01 DOI: 10.1109/EMBC.2019.8856390

Jianye Sui, Neeru Gandotra, C. Scharfe, M. Javanmard

引用次数: 0

A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification 1-D与2-D深度卷积神经网络在心电分类中的比较

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-10-16 DOI: 10.1109/EMBC.2018.8512242

Yunan Wu, Feng Yang, Y. Liu, Xuefan Zha, Shaofeng Yuan

{"title":"A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification","authors":"Yunan Wu, Feng Yang, Y. Liu, Xuefan Zha, Shaofeng Yuan","doi":"10.1109/EMBC.2018.8512242","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8512242","url":null,"abstract":"Effective detection of arrhythmia is an important task in the remote monitoring of electrocardiogram (ECG). The traditional ECG recognition depends on the judgment of the clinicians' experience, but the results suffer from the probability of human error due to the fatigue. To solve this problem, an ECG signal classification method based on the images is presented to classify ECG signals into normal and abnormal beats by using two-dimensional convolutional neural networks (2D-CNNs). First, we compare the accuracy and robustness between one-dimensional ECG signal input method and two-dimensional image input method in AIexNet network. Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AIexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification. The performance evaluated on the MIT-BIH arrhythmia database demonstrates that the proposed method can achieve the accuracy of 98% and maintain high accuracy within SNR range from 20 dB to 35 dB. The experiment shows that the 2D-CNNs initialized with AIexNet weights performs better than one-dimensional signal method without a large-scale dataset.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"38 1","pages":"324-327"},"PeriodicalIF":0.0,"publicationDate":"2018-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83001229","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: 72

Brain Morphometry Analysis with Surface Foliation Theory 用表面叶理理论分析脑形态

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-09-08 DOI: 10.1109/EMBC.2018.8512198

Chengfeng Wen, Na Lei, Ming Ma, Xin Qi, Wen Zhang, Yalin Wang, X. Gu

{"title":"Brain Morphometry Analysis with Surface Foliation Theory","authors":"Chengfeng Wen, Na Lei, Ming Ma, Xin Qi, Wen Zhang, Yalin Wang, X. Gu","doi":"10.1109/EMBC.2018.8512198","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8512198","url":null,"abstract":"Brain morphometry study plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features rooted in surface foliation theory. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer's disease and healthy control subjects demonstrate the efficiency and efficacy of our method.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"135 1","pages":"123-126"},"PeriodicalIF":0.0,"publicationDate":"2018-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75761654","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

Low-Cost, USB Connected and Multi-Purpose Biopotential Recording System. 低成本，USB连接和多用途的生物电位记录系统。

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-07-01 DOI: 10.1109/EMBC.2018.8513301

Han Sun, Jiayang Liu, Kelilah L Wolkowicz, Xiong Zhang, B. Gluckman

{"title":"Low-Cost, USB Connected and Multi-Purpose Biopotential Recording System.","authors":"Han Sun, Jiayang Liu, Kelilah L Wolkowicz, Xiong Zhang, B. Gluckman","doi":"10.1109/EMBC.2018.8513301","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8513301","url":null,"abstract":"Several research arenas and clinical applications are reliant on biopotential recordings, such as electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), and neural interfaces including brain computer interface (BCI). Here, we present a low-cost, biopotential, acquisition hardware platform board (PSUEEG platform) suitable for a wide range of recording tasks. Implementations of the hardware include applications requiring 8 or 16 channels of biopotential recordings, and 3-axis accelerometer measurements, among other modalities. The device firmware allows for flexible software configuration through USB. Power and data are transmitted between the device and base computer through an electrically isolated USB. The device is compatible with a range of computer operating systems, including Windows, Linux, and OSX. Additionally, we have crafted data acquisition under a range of programming platforms, including C++, Python, MATLAB Simulink, and LabView. Notably, we have demonstrated the interface with the Matlab PsychToolbox and the popular BCI2000 platform. The acquisition system with can be used in educational and research-based applications, neural interfaces, and clinical interfaces. For education and research, we have utilized this platform in BCI work, as well as demonstrated comparable classification performance for different paradigms.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"42 1","pages":"4359-4362"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75931265","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

Arrhythmia Classification from Single Lead ECG by Multi-Scale Convolutional Neural Networks. 基于多尺度卷积神经网络的单导联心电心律失常分类。

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-01-01 DOI: 10.1109/EMBC.2018.8512260

Zhenjie Yao, Yixin Chen

{"title":"Arrhythmia Classification from Single Lead ECG by Multi-Scale Convolutional Neural Networks.","authors":"Zhenjie Yao, Yixin Chen","doi":"10.1109/EMBC.2018.8512260","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8512260","url":null,"abstract":"Arrhythmia refers to any abnormal change from the normal electrical impulses of the heart. Some arrhythmias are manifested as abnormal heartbeat. Effective heartbeat classification is helpful for computer aided diagnosis. Conventional heartbeat classification methods work on information of multiple leads, and need heuristic or hand-crafted feature extraction. In this paper, we propose a new heartbeat classification approach based on a recent deep learning architecture called multi-scale convolutional neural networks (MCNN). A unique feature of our work is that we take single lead ECG as input, rhythm information is not taken into consideration. Such a single-lead setting, although more challenging than multi-lead cases, is often faced in medical practice due to advancements in mobile ECG devices and hence much needed. We exploit the power of convolutional neural networks for find discriminative features in heartbeat time series. The algorithm was tested on public datasets. The overall accuracy is 0.8866, the accuracy on supraventricular ectopic beat is 0.9600, and accuracy on ventricular ectopic beat is 0.9250. The performance is comparable with conventional method using features hand crafted by human experts.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"49 1","pages":"344-347"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90375822","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: 4

Diagnostic Classification of Autism using Resting-State fMRI Data and Conditional Random Forest. 基于静息状态fMRI数据和条件随机森林的自闭症诊断分类。

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-01-01 DOI: 10.1109/EMBC.2018.8512502

A. R. J. Fredo, Afrooz Jahedi, M. Reiter, Ralph-Axel Muller

{"title":"Diagnostic Classification of Autism using Resting-State fMRI Data and Conditional Random Forest.","authors":"A. R. J. Fredo, Afrooz Jahedi, M. Reiter, Ralph-Axel Muller","doi":"10.1109/EMBC.2018.8512502","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8512502","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is associated with atypical connectivity within and between brain regions. In this study, we attempted to classify functional Magnetic Resonance Images (fMRI) of Typically Developing (TD) and ASD participants using conditional random forest and random forest. Restingstate fMRI images of TD and ASD participants (N=320 for training and N=80 for validation) were obtained from the Autism Imaging Data Exchange; ABIDE-I, ABIDE-II. Images were preprocessed using a standard pipeline. A Functional Connectivity (FC) matrix was calculated using 237 cortical, subcortical, and cerebellar Regions of Interest (ROIs). The dimensionality of the FC matrix was reduced using conditional random forests and at each dimension classification accuracy was tested using random forests. Results suggest that in the current dataset, the random forest is able to classify the TD and ASD with a peak accuracy of 65% using 143 features. Remarkably, the Cingulo-Opercular Task Control (COTC) region contributed the highest number of features linked to more accurate classification, and connectivity between COTC and the dorsal attention network distinguished ASD and TD participants.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"54 3 1","pages":"1148-1151"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90673735","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: 27

An Application of Conditional Robust Calibration (CRC) to The Lotka-Volterra Predator-Prey model in computational systems biology: a comparison of two sampling strategies. 条件鲁棒校准(CRC)在计算系统生物学中Lotka-Volterra捕食者-猎物模型中的应用:两种采样策略的比较。

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-01-01 DOI: 10.1109/EMBC.2018.8512744

F. Bianconi, C. Antonini, L. Tomassoni, P. Valigi

{"title":"An Application of Conditional Robust Calibration (CRC) to The Lotka-Volterra Predator-Prey model in computational systems biology: a comparison of two sampling strategies.","authors":"F. Bianconi, C. Antonini, L. Tomassoni, P. Valigi","doi":"10.1109/EMBC.2018.8512744","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8512744","url":null,"abstract":"Mathematical modeling is a widely used technique for describing the temporal behavior of biological systems. One of the most challenging topics in computational systems biology is the calibration of nonlinear models, i.e. the estimation of their unknown parameters. The state of the art methods in this field are the frequentist and Bayesian approaches. For both of them, the performances and accuracy of results highly depend on the sampling technique employed. Here, we test a novel Bayesian procedure for parameter estimation, called Conditional Robust Calibration (CRC), comparing two different sampling techniques: uniform and logarithmic Latin Hypercube Sampling (LHS). CRC is an iterative algorithm based on parameter space sampling and on the estimation of parameter density functions. We apply CRC with both sampling strategies to the Lotka-Volterra model and we obtain a more precise and reliable solution through logarithmically spaced samples.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"27 1","pages":"2358-2361"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78032196","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

Preliminary Finite Element Model for Hydrogen Peroxide-based Glucose Sensors. 基于过氧化氢的葡萄糖传感器的初步有限元模型。

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-01-01 DOI: 10.1109/EMBC.2018.8513360

John Valdovinos

{"title":"Preliminary Finite Element Model for Hydrogen Peroxide-based Glucose Sensors.","authors":"John Valdovinos","doi":"10.1109/EMBC.2018.8513360","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8513360","url":null,"abstract":"The development of continuous glucose monitoring and insulin control algorithms have enabled the recent development of closed-loop artificial pancreas technology. However, despite these advancements, glucose sensor accuracy and reliability under physiologic conditions and over long periods of monitoring continue to be limiting factors in achieving a truly closed-loop artificial pancreas. To develop improved sensor technology, glucose sensor dynamics and performance need to be modeled accurately under physiologic conditions. A three dimensional hydrogen-based glucose sensor model was developed to predict steady-state sensor performance. The finite element model, which included a three-electrode system and relevant electrochemical reactions for electrochemical current calculation, was developed on COMSOL Multiphysics software. The results were validated using an experimental setup measuring various hydrogen peroxide concentrations ranging from 5 mM to 35 mM. The model predicted a linear relationship between current ranging from $5 . 1 mu A$ to $35 . 8 mu A$ for the aforementioned glucose concentrations. Experimental data demonstrated a linear relationship between hydrogen peroxide concentration within the same range, and current measurements ranging from $9 . 4 mu A$ to $60 . 6 mu A$. The model and experimental data differed consistently by percentages between 40-46 % for all concentrationstested. This consistent scaling error can be attributed to the difference in electrode geometric area and electrochemical active area. Future iterations of the model must take into consideration the effective electrode area.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"8 1","pages":"4301-4304"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86913813","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

Eye Movements of Patients with MCI against Wrong-Way Driving Countermeasures. MCI患者眼动对错误驾驶对策的影响

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-01-01 DOI: 10.1109/EMBC.2018.8512601

T. Nagao, M. Nihei, M. Kamata, A. Tamai, H. Nakagawa, M. Goto, Y. Nagami, K. Matsushita

{"title":"Eye Movements of Patients with MCI against Wrong-Way Driving Countermeasures.","authors":"T. Nagao, M. Nihei, M. Kamata, A. Tamai, H. Nakagawa, M. Goto, Y. Nagami, K. Matsushita","doi":"10.1109/EMBC.2018.8512601","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8512601","url":null,"abstract":"Wrong-way driving on highways is an important issue in many countries as it can potentially put the lives of many at risk. In Japan, approximately 200 instances of wrong-way driving occur annually, and preventative countermeasures, such as road arrows, have been implemented. However, the incidence of wrong-way driving has not decreased since the introduction of these countermeasures, and stronger countermeasures are therefore necessary. More than 70% of wrong-way drivers are elderly individuals, and, in Japan, over 30% of elderly individuals have diseases leading to cognitive decline. In this paper, we focus on the reduction of visual cognitive function due to mild cognitive impairment (MCI), and the effects of visual countermeasures on patients with MCI, as determined using a computer graphics movie and an infrared eye tracker to investigate gaze movements. We analyzed differences in fixation points and the quantity of saccades between patients with MCI and healthy individuals. Patients with MCI were found to have delayed identification of wrong-way driving. This suggests that deficits in visual attention and deterioration of visual cognitive function in dynamic environments may be factors underlying wrong-way driving in patients with MCI.","PeriodicalId":72689,"journal":{"name":"Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference","volume":"168 1","pages":"2080-2083"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77926818","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

A Fast Respiratory Rate Estimation Method using Joint Sparse Signal Reconstruction based on Regularized Sparsity Adaptive Matching Pursuit. 一种基于正则稀疏度自适应匹配追踪的联合稀疏信号重构呼吸频率快速估计方法。

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Pub Date : 2018-01-01 DOI: 10.1109/EMBC.2018.8512897

Zhongyi Han, Qun Wang, Liang Yue, Zhiwen Liu

引用次数: 0