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
Pub Date : 2019-07-01DOI: 10.1109/EMBC.2019.8856390
Jianye Sui, Neeru Gandotra, C. Scharfe, M. Javanmard
DNA quantification and characterization are of critical importance in disease diagnosis and clinical analysis, while label-free technology greatly simplifies the sensing protocol as it eliminates the extra step for attaching the indicator to DNA strands. In this work, we present a novel label-free DNA detection methodology based on electrical frequency-dependent impedance. The impedance of DNA strands conjunct with streptavidin-coated magnetic beads was measured at 8 different frequencies using an electrical impedance sensor integrated on a chip. Different concentrations of 300 bp double-stranded DNA samples were used to validate our sensor. The minimum DNA amount that could be successfully detected was 0.77 ng (3.9 amol). Detecting DNA fragments using our sensor could be further reduced from currently 20 minutes to under 15 minutes.
DNA定量和表征在疾病诊断和临床分析中至关重要，而无标记技术极大地简化了传感方案，因为它消除了将指示剂附着在DNA链上的额外步骤。在这项工作中，我们提出了一种基于电频率相关阻抗的新型无标记DNA检测方法。利用集成在芯片上的电阻抗传感器，在8种不同频率下测量了DNA链与链亲和素涂层磁珠结合的阻抗。使用不同浓度的300 bp双链DNA样本来验证我们的传感器。成功检测到的最小DNA量为0.77 ng (3.9 amol)。使用我们的传感器检测DNA片段可以进一步缩短，从目前的20分钟缩短到15分钟以下。
{"title":"Rapid Label-free DNA Quantification by Multi-frequency Impedance Sensing on a Chip.","authors":"Jianye Sui, Neeru Gandotra, C. Scharfe, M. Javanmard","doi":"10.1109/EMBC.2019.8856390","DOIUrl":"https://doi.org/10.1109/EMBC.2019.8856390","url":null,"abstract":"DNA quantification and characterization are of critical importance in disease diagnosis and clinical analysis, while label-free technology greatly simplifies the sensing protocol as it eliminates the extra step for attaching the indicator to DNA strands. In this work, we present a novel label-free DNA detection methodology based on electrical frequency-dependent impedance. The impedance of DNA strands conjunct with streptavidin-coated magnetic beads was measured at 8 different frequencies using an electrical impedance sensor integrated on a chip. Different concentrations of 300 bp double-stranded DNA samples were used to validate our sensor. The minimum DNA amount that could be successfully detected was 0.77 ng (3.9 amol). Detecting DNA fragments using our sensor could be further reduced from currently 20 minutes to under 15 minutes.","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":"111 1","pages":"5670-5673"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89451862","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 : 2018-10-16DOI: 10.1109/EMBC.2018.8512242
Yunan Wu, Feng Yang, Y. Liu, Xuefan Zha, Shaofeng Yuan
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.
{"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}
Pub Date : 2018-09-08DOI: 10.1109/EMBC.2018.8512198
Chengfeng Wen, Na Lei, Ming Ma, Xin Qi, Wen Zhang, Yalin Wang, X. Gu
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.
{"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}
Pub Date : 2018-07-01DOI: 10.1109/EMBC.2018.8513301
Han Sun, Jiayang Liu, Kelilah L Wolkowicz, Xiong Zhang, B. Gluckman
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.
{"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}
Pub Date : 2018-01-01DOI: 10.1109/EMBC.2018.8512260
Zhenjie Yao, Yixin Chen
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.
{"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}
Pub Date : 2018-01-01DOI: 10.1109/EMBC.2018.8512502
A. R. J. Fredo, Afrooz Jahedi, M. Reiter, Ralph-Axel Muller
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.
自闭症谱系障碍(ASD)是一种神经发育障碍，与大脑区域内部和之间的非典型连通性有关。在本研究中，我们尝试使用条件随机森林和随机森林对典型发育(TD)和ASD参与者的功能磁共振图像(fMRI)进行分类。从自闭症成像数据交换中获得TD和ASD参与者的静息状态fMRI图像(N=320用于训练，N=80用于验证);ABIDE-I ABIDE-II。使用标准管道对图像进行预处理。使用237个皮质、皮质下和小脑感兴趣区(roi)计算功能连通性(FC)矩阵。使用条件随机森林对FC矩阵进行降维，并在每个维度上使用随机森林对分类精度进行测试。结果表明，在当前数据集中，随机森林能够使用143个特征对TD和ASD进行分类，峰值准确率达到65%。值得注意的是，cingulo - opcular Task Control (COTC)区域贡献了最多与更准确分类相关的特征，并且COTC和背侧注意网络之间的连通性区分了ASD和TD参与者。
{"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}
Pub Date : 2018-01-01DOI: 10.1109/EMBC.2018.8512744
F. Bianconi, C. Antonini, L. Tomassoni, P. Valigi
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.
{"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}
Pub Date : 2018-01-01DOI: 10.1109/EMBC.2018.8513360
John Valdovinos
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.
连续血糖监测和胰岛素控制算法的发展使得闭环人工胰腺技术的发展成为可能。然而，尽管取得了这些进展，葡萄糖传感器在生理条件下和长时间监测下的准确性和可靠性仍然是实现真正闭环人工胰腺的限制因素。为了改进传感器技术，需要在生理条件下准确地模拟葡萄糖传感器的动态和性能。建立了三维氢基葡萄糖传感器模型，用于预测传感器的稳态性能。在COMSOL Multiphysics软件上建立了包含三电极体系和相关电化学反应的有限元模型，用于电化学电流计算。使用实验装置测量了从5毫米到35毫米的各种过氧化氢浓度，结果得到了验证。该模型预测了电流从5美元到35毫米之间的线性关系。1美元到35美元。8 mu A$表示上述葡萄糖浓度。实验数据表明，在相同的范围内，过氧化氢浓度与当前的测量值之间存在线性关系，范围从$9。4美元到60美元。6 mu A$。模型和实验数据在所有浓度测试中始终存在40- 46%之间的百分比差异。这种一致的标度误差可归因于电极几何面积和电化学活性面积的差异。模型的未来迭代必须考虑有效电极面积。
{"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}
Pub Date : 2018-01-01DOI: 10.1109/EMBC.2018.8512601
T. Nagao, M. Nihei, M. Kamata, A. Tamai, H. Nakagawa, M. Goto, Y. Nagami, K. Matsushita
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.
{"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}
Pub Date : 2018-01-01DOI: 10.1109/EMBC.2018.8512897
Zhongyi Han, Qun Wang, Liang Yue, Zhiwen Liu
Many algorithms have been used to estimate respiratory rate (RR) from Photoplethysmography (PPG) recently. However, the accuracy and time consumption are still a challenging issue. In this paper, we propose a novel algorithm for RR estimation using Joint Sparse Signal Reconstruction (JSSR) based on Regularized Sparsity Adaptive Matching Pursuit (RSAMP) in a real-time fashion. The algorithm has been tested on Capnobase dataset and the results showed that the mean absolute error (MAE) and root mean squared error between estimates and references are 1.09 breaths per minute (bpm) and 2.44 bpm, respectively. And our method only costs 0.54 seconds for calculation.
{"title":"A Fast Respiratory Rate Estimation Method using Joint Sparse Signal Reconstruction based on Regularized Sparsity Adaptive Matching Pursuit.","authors":"Zhongyi Han, Qun Wang, Liang Yue, Zhiwen Liu","doi":"10.1109/EMBC.2018.8512897","DOIUrl":"https://doi.org/10.1109/EMBC.2018.8512897","url":null,"abstract":"Many algorithms have been used to estimate respiratory rate (RR) from Photoplethysmography (PPG) recently. However, the accuracy and time consumption are still a challenging issue. In this paper, we propose a novel algorithm for RR estimation using Joint Sparse Signal Reconstruction (JSSR) based on Regularized Sparsity Adaptive Matching Pursuit (RSAMP) in a real-time fashion. The algorithm has been tested on Capnobase dataset and the results showed that the mean absolute error (MAE) and root mean squared error between estimates and references are 1.09 breaths per minute (bpm) and 2.44 bpm, respectively. And our method only costs 0.54 seconds for calculation.","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":"29 1","pages":"2849-2852"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74634629","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}
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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