Pub Date : 2009-10-30DOI: 10.1109/BMEI.2009.5305032
Hai-Yang Wang, Min Ding, Xia Li, Bairong Shen
Inferring tumor subtypes based on the gene expression data alone does not appear to be as powerful as expected for the lack of robustness and clinical meaning. The ultimate aim of clustering tumor samples should be to support clinical evaluation or treatment. Therefore, clustering procedure should closely integrate the clinical outcome and/or treatment information for final representation of the tumor homogeneity and heterogeneity. In this work, we developed an ensemble clustering method guided by the clinical outcome and treatment information for the identification of the robust and clinically meaningful tumor subtypes. Our method was expected to yield more robust and clinically relevant results than other commonly used methods and to give us comprehensive understanding of tumor heterogeneity. Keywords-Ensemble clustering, survival analysis, tumor heterogeneity, clinical outcome
{"title":"Clinical Information Driven Ensemble Clustering for Inferring Robust Tumor Subtypes","authors":"Hai-Yang Wang, Min Ding, Xia Li, Bairong Shen","doi":"10.1109/BMEI.2009.5305032","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305032","url":null,"abstract":"Inferring tumor subtypes based on the gene expression data alone does not appear to be as powerful as expected for the lack of robustness and clinical meaning. The ultimate aim of clustering tumor samples should be to support clinical evaluation or treatment. Therefore, clustering procedure should closely integrate the clinical outcome and/or treatment information for final representation of the tumor homogeneity and heterogeneity. In this work, we developed an ensemble clustering method guided by the clinical outcome and treatment information for the identification of the robust and clinically meaningful tumor subtypes. Our method was expected to yield more robust and clinically relevant results than other commonly used methods and to give us comprehensive understanding of tumor heterogeneity. Keywords-Ensemble clustering, survival analysis, tumor heterogeneity, clinical outcome","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"107 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85997157","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305564
Chao Wang, C. Xu, Ming Zhang, W. Yin
The bio-impedance measurement method via electromagnetic induction has the non-contact character which overcomes influences of the electrode's contact impedance to measurements. However its measurement results have a highly complicated relationship with impedance properties of measured objects. In order to discover the regularity, this paper deduces an analytical resolution model using a probe-coil and analyzes affections of conductivity and lift-off to mutual inductance variation. The achieved disciplinary characters are valuable not only to this measurement method using the probe-coil but also to the data analysis in Electromagnetic Tomography (EMT).
{"title":"Analysis of Bio-Impedance Measurement Method via Electromagnetic Induction Using Probe-Coil","authors":"Chao Wang, C. Xu, Ming Zhang, W. Yin","doi":"10.1109/BMEI.2009.5305564","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305564","url":null,"abstract":"The bio-impedance measurement method via electromagnetic induction has the non-contact character which overcomes influences of the electrode's contact impedance to measurements. However its measurement results have a highly complicated relationship with impedance properties of measured objects. In order to discover the regularity, this paper deduces an analytical resolution model using a probe-coil and analyzes affections of conductivity and lift-off to mutual inductance variation. The achieved disciplinary characters are valuable not only to this measurement method using the probe-coil but also to the data analysis in Electromagnetic Tomography (EMT).","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"13 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76904277","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305454
Dan-dan Yan, Jing Li
This paper focuses on the inhomogeneous conductivity reconstruction of human head tissues by means of magnetic resonance electrical impedance tomography (MREIT). MREIT is a recently introduced and non-invasive conductivity imaging modality that combines Current Density Imaging (CDI) and traditional Electrical Impedance Tomography (EIT) techniques. MREIT, designed to deal with the well-known ill-posed problem in traditional EIT, has been applied to reconstruct the conductivities of human head tissues. We have developed two realistic geometry finite element method (FEM) head models, with five tissues including the scalp, skull, CSF, gray matter and white matter, based on the hexahedral element and the tetrahedral element, respectively. The J-substitution MREIT algorithm is used in our simulation for its easy realization. The present simulation results show that the MREIT algorithm combined with the realistic geometry FEM head model can reconstruct the inhomogeneous human head tissue conductivity distributions with higher accuracy. Our work so far suggests that the proposed MREIT algorithms can provide useful conductivity information for solving the EEG/MEG forward/inverse problems, and for further investigations on human head tissues using MREIT.
{"title":"Conductivity Reconstruction of Human Head Tissues by Means of MREIT","authors":"Dan-dan Yan, Jing Li","doi":"10.1109/BMEI.2009.5305454","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305454","url":null,"abstract":"This paper focuses on the inhomogeneous conductivity reconstruction of human head tissues by means of magnetic resonance electrical impedance tomography (MREIT). MREIT is a recently introduced and non-invasive conductivity imaging modality that combines Current Density Imaging (CDI) and traditional Electrical Impedance Tomography (EIT) techniques. MREIT, designed to deal with the well-known ill-posed problem in traditional EIT, has been applied to reconstruct the conductivities of human head tissues. We have developed two realistic geometry finite element method (FEM) head models, with five tissues including the scalp, skull, CSF, gray matter and white matter, based on the hexahedral element and the tetrahedral element, respectively. The J-substitution MREIT algorithm is used in our simulation for its easy realization. The present simulation results show that the MREIT algorithm combined with the realistic geometry FEM head model can reconstruct the inhomogeneous human head tissue conductivity distributions with higher accuracy. Our work so far suggests that the proposed MREIT algorithms can provide useful conductivity information for solving the EEG/MEG forward/inverse problems, and for further investigations on human head tissues using MREIT.","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"15 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76998614","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305554
Qian Chang, Jun Shi, Zhiheng Xiao
Three-dimension (3D) based image data analysis has an important role for significantly improving the detection and diagnosis of lung disease with computed tomography (CT). In this paper, we proposed a new volume-based 3D segmentation algorithm based on the extended 3D pulse coupled neural network (PCNN) model. This algorithm was successfully used to segment the lung field in CT slice with the mean distance, root means square distance and Tanimoto coefficient of 0.0029±0.0005, 0.0715±0.0056, 0.9760±0.0093, respectively. Furthermore, the means running time was only 273s, which was much less than those of 2D PCNN segmentation algorithm and Otsu algorithm. The experimental results demonstrated the extended 3D PCNN segmentation algorithm had the advantage of short execution time with good segmentation accuracy. The results suggest that the proposed 3D PCNN algorithm can be potentially used for lung computer-aided diagnosis.
{"title":"A New 3D Segmentation Algorithm Based on 3D PCNN for Lung CT Slices","authors":"Qian Chang, Jun Shi, Zhiheng Xiao","doi":"10.1109/BMEI.2009.5305554","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305554","url":null,"abstract":"Three-dimension (3D) based image data analysis has an important role for significantly improving the detection and diagnosis of lung disease with computed tomography (CT). In this paper, we proposed a new volume-based 3D segmentation algorithm based on the extended 3D pulse coupled neural network (PCNN) model. This algorithm was successfully used to segment the lung field in CT slice with the mean distance, root means square distance and Tanimoto coefficient of 0.0029±0.0005, 0.0715±0.0056, 0.9760±0.0093, respectively. Furthermore, the means running time was only 273s, which was much less than those of 2D PCNN segmentation algorithm and Otsu algorithm. The experimental results demonstrated the extended 3D PCNN segmentation algorithm had the advantage of short execution time with good segmentation accuracy. The results suggest that the proposed 3D PCNN algorithm can be potentially used for lung computer-aided diagnosis.","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"22 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77149016","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5302035
Tingting Fu, Yihui Liu, Jinyong Cheng, Qiang Liu, Baopeng Li
SVM (Support Vector Machine) is a new machinelearning technique which is developed based on statistical theory and it is applied in the various fields in recent years. We use SVM model based on P MRS (Phosphorus magnetic resonance spectroscopy) data to distinguish three categories of hepatocellular carcinoma, hepatic cirrhosis and normal hepatic tissue. The recognition accuracy of the three categories was obtained, and the classification accuracy of SVM based on polynomial and radial basis function kernel is compared. The result of experiments shows that SVM model based on P MRS data provides diagnostic prediction of liver in vivo, and the performance based on polynomial is better than based on radial basis function kernel. KeywordsSVM; 31P MRS; Kernel Function; Hepatocellular Carcinoma
支持向量机是近年来在统计理论的基础上发展起来的一种新的机器学习技术，在各个领域得到了广泛的应用。我们使用基于P MRS(磷磁共振波谱)数据的SVM模型来区分肝细胞癌、肝硬化和正常肝组织三种类型。得到了三类的识别精度，并比较了基于多项式和径向基函数核的支持向量机的分类精度。实验结果表明，基于P - MRS数据的SVM模型能够提供活体肝脏的诊断预测，且基于多项式的性能优于基于径向基函数核的性能。KeywordsSVM;31 p MRS;核函数;肝细胞癌
{"title":"31P MRS Data Diagnosis of Hepatocellular Carcinoma Based on Support Vector Machine","authors":"Tingting Fu, Yihui Liu, Jinyong Cheng, Qiang Liu, Baopeng Li","doi":"10.1109/BMEI.2009.5302035","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5302035","url":null,"abstract":"SVM (Support Vector Machine) is a new machinelearning technique which is developed based on statistical theory and it is applied in the various fields in recent years. We use SVM model based on P MRS (Phosphorus magnetic resonance spectroscopy) data to distinguish three categories of hepatocellular carcinoma, hepatic cirrhosis and normal hepatic tissue. The recognition accuracy of the three categories was obtained, and the classification accuracy of SVM based on polynomial and radial basis function kernel is compared. The result of experiments shows that SVM model based on P MRS data provides diagnostic prediction of liver in vivo, and the performance based on polynomial is better than based on radial basis function kernel. KeywordsSVM; 31P MRS; Kernel Function; Hepatocellular Carcinoma","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"33 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81006567","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305186
Weiwei Song, S. Cai, Bo Yang, W. Cui, Yanfang Wang
Recently, non-contact measurement technology has improved significantly. With the increasing of the accuracy and the speed of data acquisition of 3D laser scanners, the amount of point data has increased dramatically . 3D laser scanners generate up to thousands of points per second, which have become a burden of both computation and store of the data. It is quite important, therefore, to reduce the amount of acquire point data and convert them into formats required by reconstruction processes while maintaining the accuracy. In this paper, we presented a convenient way to solve the problem. The scattered point cloud data is first regularized and compressed by the octree structure and then reduced further according to a curvature rule. Compared with the other reduction methods, the method presented in this paper not only reduced the arithmetic complication on space and time , but also preserved the characteristic of the original object and finished the data reduction quickly. This paper presents a novel approach of point cloud reduction based on octree structure and curvature rule. The proposed method not only reduces the amount of point data and computational complexity but also makes the point cloud data be organized, which makes it easy to be traversed and searched in reconstruction process. The proposed methods are applied to different types of surfaces and the results are discussed.
{"title":"A Reduction Method of Three-Dimensional Point Cloud","authors":"Weiwei Song, S. Cai, Bo Yang, W. Cui, Yanfang Wang","doi":"10.1109/BMEI.2009.5305186","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305186","url":null,"abstract":"Recently, non-contact measurement technology has improved significantly. With the increasing of the accuracy and the speed of data acquisition of 3D laser scanners, the amount of point data has increased dramatically . 3D laser scanners generate up to thousands of points per second, which have become a burden of both computation and store of the data. It is quite important, therefore, to reduce the amount of acquire point data and convert them into formats required by reconstruction processes while maintaining the accuracy. In this paper, we presented a convenient way to solve the problem. The scattered point cloud data is first regularized and compressed by the octree structure and then reduced further according to a curvature rule. Compared with the other reduction methods, the method presented in this paper not only reduced the arithmetic complication on space and time , but also preserved the characteristic of the original object and finished the data reduction quickly. This paper presents a novel approach of point cloud reduction based on octree structure and curvature rule. The proposed method not only reduces the amount of point data and computational complexity but also makes the point cloud data be organized, which makes it easy to be traversed and searched in reconstruction process. The proposed methods are applied to different types of surfaces and the results are discussed.","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"22 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78004513","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305551
Hengyi Xu, Hua Wei, Zoraida P. Aguilar, Jamie L. Waldron, Andrew Z. Wang
a brighter signal compared to the organic dye using similar parameters and the same number of cells. Future directions will involve elimination of non-specific signal as well as quantification to establish the limit of detection for the QD-based cell sensing.
{"title":"Application of Semiconductor Quantum Dots for Breast Cancer Cell Sensing","authors":"Hengyi Xu, Hua Wei, Zoraida P. Aguilar, Jamie L. Waldron, Andrew Z. Wang","doi":"10.1109/BMEI.2009.5305551","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305551","url":null,"abstract":"a brighter signal compared to the organic dye using similar parameters and the same number of cells. Future directions will involve elimination of non-specific signal as well as quantification to establish the limit of detection for the QD-based cell sensing.","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73416515","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305530
Yan Jiang, Chao Xu
Based on the analysis of some literature, this paper proposes a multi-criteria evaluation model for breast cancer susceptibility gene and implements the evaluation using Cytoscape. Most of the data come from the online supplementary table of the literature, and a little missing is completed by the BiNGO plugin of Cytoscape. The Pub Med literature search shows CDC2 gene ranked first in our final evaluation list showed by Cytoscape has a close relation with breast cancer genes. Meanwhile TopBP1 gene ranked second and HMMR gene ranked sixth are proposed as breast cancer susceptibility genes by previous research. This shows our multi-criteria evaluation model can represent the complex relationship between genes and breast cancer susceptibly correctly. So other genes in the evaluation result should also be focused on. Besides, the practical application indicates that Cytoscape is a powerful biological analytical tool. Multi-criteria decision method is mainly used to resolve the problem with multi-criteria, the characteristic of which is the non-commensurable and incompatibility between objectives. The relationship between human genes is complicated, so some criteria are needed to find out those relationships and evaluate them. The previous research has given out the evaluation measure. Consequently multi-objective evaluation can be applied to analyze the relationship between researched gene and known breast cancer genes. Multi-criteria evaluation is a method to analyze the multi- criteria decision problem. The main procedures are: determine evaluation criterion, identify attribute, construct alternative set, estimate parameter, and evaluate by some calculation method.
{"title":"Evaluation Model for Breast Cancer Susceptibly Gene and its Implementation Using Cytoscape","authors":"Yan Jiang, Chao Xu","doi":"10.1109/BMEI.2009.5305530","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305530","url":null,"abstract":"Based on the analysis of some literature, this paper proposes a multi-criteria evaluation model for breast cancer susceptibility gene and implements the evaluation using Cytoscape. Most of the data come from the online supplementary table of the literature, and a little missing is completed by the BiNGO plugin of Cytoscape. The Pub Med literature search shows CDC2 gene ranked first in our final evaluation list showed by Cytoscape has a close relation with breast cancer genes. Meanwhile TopBP1 gene ranked second and HMMR gene ranked sixth are proposed as breast cancer susceptibility genes by previous research. This shows our multi-criteria evaluation model can represent the complex relationship between genes and breast cancer susceptibly correctly. So other genes in the evaluation result should also be focused on. Besides, the practical application indicates that Cytoscape is a powerful biological analytical tool. Multi-criteria decision method is mainly used to resolve the problem with multi-criteria, the characteristic of which is the non-commensurable and incompatibility between objectives. The relationship between human genes is complicated, so some criteria are needed to find out those relationships and evaluate them. The previous research has given out the evaluation measure. Consequently multi-objective evaluation can be applied to analyze the relationship between researched gene and known breast cancer genes. Multi-criteria evaluation is a method to analyze the multi- criteria decision problem. The main procedures are: determine evaluation criterion, identify attribute, construct alternative set, estimate parameter, and evaluate by some calculation method.","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"84 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77222051","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305118
Bo Huang, Kuanquan Wang, Xiangqian Wu, Dongyu Zhang, Naimin Li
Tongue diagnosis is a distinctive and essential diagnostic method. The color category of the tongue can be utilized to discover pathological changes on the tongues for identifying diseases. In this paper, a novel scheme is established which classify tongue images into various categories, including coating and substance categories. Firstly, we proposed a two level hierarch clustering method for quantizing all pixels into numerous vectors of feature value. Each vector can code a very small sub-class in RGB color space. Secondly, we utilized the vectors' distribution of these sub-classes to represent approximate chromatic information of tongue images. Then, a Bayesian Network is employed to model the relationship between these quantized vectors and tongue color categories. The effectiveness of this scheme is tested on a group of 418 tongue images, and the classification results are reported.
{"title":"Quantified Vector Oriented Tongue Color Classification","authors":"Bo Huang, Kuanquan Wang, Xiangqian Wu, Dongyu Zhang, Naimin Li","doi":"10.1109/BMEI.2009.5305118","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305118","url":null,"abstract":"Tongue diagnosis is a distinctive and essential diagnostic method. The color category of the tongue can be utilized to discover pathological changes on the tongues for identifying diseases. In this paper, a novel scheme is established which classify tongue images into various categories, including coating and substance categories. Firstly, we proposed a two level hierarch clustering method for quantizing all pixels into numerous vectors of feature value. Each vector can code a very small sub-class in RGB color space. Secondly, we utilized the vectors' distribution of these sub-classes to represent approximate chromatic information of tongue images. Then, a Bayesian Network is employed to model the relationship between these quantized vectors and tongue color categories. The effectiveness of this scheme is tested on a group of 418 tongue images, and the classification results are reported.","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"15 5 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82585833","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 : 2009-10-30DOI: 10.1109/BMEI.2009.5305322
Bei Wang, T. Sugi, Xingyu Wang, A. Ikeda, T. Nagamine, H. Shibasaki, Masatoshi Nakamura
EEG is represented to referential derivation with ear lobe reference and bipolar derivations for quantitative inter- pretation. When the ear lobe was activated, referential derivation with ear lobe reference was contaminated by ear lobe artifacts. In this study, the focus and extension of EEG components were estimated based on the cross spectrum of bipolar derivation. A referential derivation was constructed by choosing the electrodes out of the focus and extension area to obtain the distribution of EEG components. The constructed referential derivation can avoid the artifacts of ear lobe activation for quantitative EEG interpretation. Keywords-cross spectrum; bipolar derivation; focal and extension; automatic EEG interpretation; slowing wave
{"title":"Bipolar EEG Analysis Based on Cross Spectrum: Focal Detection of Slowing Wave for Automatic EEG Interpretation","authors":"Bei Wang, T. Sugi, Xingyu Wang, A. Ikeda, T. Nagamine, H. Shibasaki, Masatoshi Nakamura","doi":"10.1109/BMEI.2009.5305322","DOIUrl":"https://doi.org/10.1109/BMEI.2009.5305322","url":null,"abstract":"EEG is represented to referential derivation with ear lobe reference and bipolar derivations for quantitative inter- pretation. When the ear lobe was activated, referential derivation with ear lobe reference was contaminated by ear lobe artifacts. In this study, the focus and extension of EEG components were estimated based on the cross spectrum of bipolar derivation. A referential derivation was constructed by choosing the electrodes out of the focus and extension area to obtain the distribution of EEG components. The constructed referential derivation can avoid the artifacts of ear lobe activation for quantitative EEG interpretation. Keywords-cross spectrum; bipolar derivation; focal and extension; automatic EEG interpretation; slowing wave","PeriodicalId":6389,"journal":{"name":"2009 2nd International Conference on Biomedical Engineering and Informatics","volume":"37 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81397099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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