The Experts below are selected from a list of 7080 Experts worldwide ranked by ideXlab platform
K. Najarian - One of the best experts on this subject based on the ideXlab platform.
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a Hierarchical Method for removal of baseline drift from biomedical signals application in ecg analysis
The Scientific World Journal, 2013Co-Authors: Rosalyn Hobson Hargraves, Ashwin Belle, Xuguang Qi, Kevin R Ward, Michael Paul Pfaffenberger, K. NajarianAbstract:Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a Hierarchical Method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.
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A new Hierarchical Method for multi-level segmentation of bone in pelvic CT scans
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Jie Wu, Pavani Davuluri, Kevin Ward, Charles Cockrell, Rosalyn Hobson, K. NajarianAbstract:Pelvic bone segmentation is a vital step in analyzing pelvic CT images and assisting physicians with diagnostic decisions in traumatic pelvic injuries. A new Hierarchical segmentation algorithm is proposed using a template-based best shape matching Method and Registered Active Shape Model (RASM) to automatically extract pelvic bone tissues from multi-level pelvic CT images. A novel Hierarchical initialization process for RASM is proposed. 449 CT images across seven patients are used to test and validate the reliability and robustness of the proposed Method. The segmentation results show that the proposed Method performs better with higher accuracy than standard ASM Method.
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CSB - A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
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A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference 2004. CSB 2004., 2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
A. Darvish - One of the best experts on this subject based on the ideXlab platform.
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CSB - A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
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A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference 2004. CSB 2004., 2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
R.h. Zadeh - One of the best experts on this subject based on the ideXlab platform.
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CSB - A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
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A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference 2004. CSB 2004., 2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
K. Gopalakrishhan - One of the best experts on this subject based on the ideXlab platform.
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CSB - A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
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A new Hierarchical Method for identification of dynamic regulatory pathways from time-series DNA microarray data
Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference 2004. CSB 2004., 2004Co-Authors: A. Darvish, K. Gopalakrishhan, R.h. Zadeh, K. NajarianAbstract:A new Hierarchical Method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the Hierarchical Method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed Method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.
Lihui Jiang - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - A Hierarchical Method for traffic sign classification with support vector machines
The 2013 International Joint Conference on Neural Networks (IJCNN), 2013Co-Authors: Gangyi Wang, Zhilu Wu, Yaqin Zhao, Lihui JiangAbstract:Traffic sign classification is an important function for driver assistance systems. In this paper, we propose a Hierarchical Method for traffic sign classification. There are two hierarchies in the Method: the first one classifies traffic signs into several super classes, while the second one further classifies the signs within their super classes and provides the final results. Two perspective adjustment Methods are proposed and performed before the second hierarchy, which significantly improves the classification accuracy. Experimental results show that the proposed Method gets an accuracy of 99.52% on the German Traffic Sign Recognition Benchmark (GTSRB), which outperforms the state-of-the-art Method. In addition, it takes about 40 ms to process one image, making it suitable for realtime applications.
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A Hierarchical Method for traffic sign classification with support vector machines
The 2013 International Joint Conference on Neural Networks (IJCNN), 2013Co-Authors: Gangyi Wang, Zhilu Wu, Yaqin Zhao, Lihui JiangAbstract:Traffic sign classification is an important function for driver assistance systems. In this paper, we propose a Hierarchical Method for traffic sign classification. There are two hierarchies in the Method: the first one classifies traffic signs into several super classes, while the second one further classifies the signs within their super classes and provides the final results. Two perspective adjustment Methods are proposed and performed before the second hierarchy, which significantly improves the classification accuracy. Experimental results show that the proposed Method gets an accuracy of 99.52% on the German Traffic Sign Recognition Benchmark (GTSRB), which outperforms the state-of-the-art Method. In addition, it takes about 40 ms to process one image, making it suitable for realtime applications.
