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Functional characterization of atrial electrograms in sinus rhythm delineates sites of parasympathetic innervation in patients with paroxysmal atrial fibrillation

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JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY 2007年第14期50卷 1324-1331页

作者： Lellouche, Nicolas Buch, Eric Celigoj, Andrew Siegerman, Carin Cesario, David De Diego, Carlos Mahajan, Aman Boyle, Noel G. Wiener, Isaac Garfinkel, Alan Shivkumar, Kalyanam Univ Calif Los Angeles UCLA Cardiac Arrhythmia Ctr Div Cardiol Dept MedDavid Geffen Sch Med Los Angeles CA 90095 USA

Objectives This study sought to characterize left atrial (LA) sinus rhythm electrogram (EGM) patterns and their relationship to parasympathetic responses during atrial fibrillation (AF) ablation. Background The mechanistic basis of fractionated LA EGMs in patients with paroxysmal AF is not well understood. Methods We analyzed 1,662 LA ablation sites from 30 patients who underwent catheter ablation for paroxysmal AF. Preablation EGM characteristics (number of deflections, amplitude, and duration) were measured in sinus rhythm. Parasympathetic responses during radiofrequency application (increase of atrial-His interval by 10 ms or decrease of sinus rate by >= 20%) were assessed at all sites. We also prospectively studied the effect of adenosine, a pharmacological agent mimicking acetylcholine signaling in myocytes, on LA EGMs. Finally, we performed mathematical simulations of atrial tissue to delineate possible mechanisms of fractionated EGMs in sinus rhythm. Results A specific pattern of pre-ablation sinus rhythm EGM (deflections >= 4, amplitude >= 0.7 mV, and duration >= 40 ms) was strongly associated with parasympathetic responses (sensitivity 72%, specificity 91%). The sites associated with these responses were found to be located mainly in the posterior wall of the LA. Adenosine administration and mathematical simulation of the effect of acetylcholine were able to reproduce a similar EGM pattern. Conclusions Parasympathetic activation during AF ablation is associated with the presence of pre-ablation high-amplitude fractionated EGMs in sinus rhythm. Local acetylcholine release could potentially explain this phenomenon.

关键词： AF atrial fibrillation A–H atrial-His CART classification and regression tree CFAE complex fractionated atrial electrogram EGM electrogram HAFE high-amplitude fractionated electrogram LA left atrial/atrium LAFE low-amplitude fractionated electrogram PV pulmonary vein RA right atrial/atrium RF radiofrequency

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Mining learners' behavior in accessing Web-based interface

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2nd International Conference on Technologies for E-Learning and Digital Entertainment (Edutainment 2007)

作者： Lee, Man Wai Chen, Sherry Y. Liu, Xiaohui Brunel Univ Sch Informat Syst Comp & Math Uxbridge UB8 3PH Middx England

ISBN: (纸本)9783540730101

Web-based technology has already been adopted as a tool to support teaching and learning in higher education. One criterion affecting the usability of such a technology is the design of web-based interface (WBI) within web-based learning programs. How different users access the WBIs has been investigated by several studies, which mainly analyze the collected data using statistical methods. In this paper, we propose to analyze users' learning behavior using Data Mining (DM) techniques. Findings in our study show that learners with different cognitive styles seem to have various learning preferences, and DM is an efficient tool to analyze the behavior of different cognitive style groups.

关键词： cognitive styles web-based learning web-based interface data mining classification and regression tree

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The classification and regression tree approach to pump failure rate analysis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2003年第1期79卷 59-67页

作者： Bevilacqua, M Braglia, M Montanari, R Univ Pisa Dipartimento Ingn Meccan Nucl & Prod Fac Ingn I-56126 Pisa Italy Univ Bologna Dipartimento Ingn Costruz Meccan Nucl Aeronaut & I-40136 Bologna Italy Univ Parma Dipartimento Ingn Ind I-43100 Parma Italy

In this article, a technique based on rule induction is suggested as non-parametric alternative to determine the expected failure rates of 143 centrifugal pumps included in a oil refinery plant and subjected to different operating conditions. At the same time, the procedure-makes it possible to determine the critical operating factors influencing the reliability of the pumps. In particular, the classification and regression tree approach is used to automatically generate rules from an extended data base of the plant concerning information about failures and operating conditions of the different facilities. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： failure rate reliability rule induction decision tree classification and regression tree

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Introduction to computer-intensive methods of data analysis in biology

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Ecology 2007年第1期88卷 266-267页

作者： N. Thompson Hobbs Colorado State University Department of Forest Rangeland and Watershed Stewardship and Natural Resource Ecology Laboratory Fort Collins Colorado 80523 E-mail: nthobbs@warnercnr.colostate.edu

The article reviews the book "Introduction to computer-intensive methods of data analysis in biology," by Derek A. Roff.

关键词： Bayesian analysis bootstrap classification and regression tree confidence interval cross-validation general additive model hypothesis test jackknife likelihood Monte-Carlo randomization methods regression statistical analysis

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Transient Stability Assessment for Induction Generator Embedded Power Systems

Transient Stability Assessment for Induction Generator Embed...

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2007 IEEE Canada Electrical Power Conference (EPC 2008)

作者： Yu-Jen Lin Rui-Xiang Ho Jing-Xiong Yang Tzu-Chen Hung Meng-Jen Chen Department of Electrical Engineering I-Shou University Kaohsiung Taiwan Department of Mechanical and Automation Engineering I-Shou University Kaohsiung Taiwan Department of Electrical Engineering National Kaohsiung University of Applied Sciences Kaohsiung Taiwan

This paper presents an approach to assessing transient stability of an interconnected power system which comprises of synchronous generator (SG) and induction generator (IG). The power systems under investigated were modelled, developed and simulated using SimPowerSystems. Fundamental analysis was carried out in an attempt to discover the transient stability characters of the given power systems. Multilayer perceptrons (MLP), and classification and regression tree (CART) were employed to assess stability status and stability performance index for both SG and IG. Comprehensive digital simulations shall justify the proposed approach.

关键词： Transient Stability Assessment Induction Generator Synchronous Generator Multilayer Perceptrons classification and regression tree

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A COMPARATIVE STUDY OF DATA MINING METHODS IN CONSUMER LOANS CREDIT SCORING MANAGEMENT

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Journal of Systems Science and Systems Engineering 2006年第4期15卷 419-435页

作者： Wenbing XIAO Qian ZHAO Qi FEI Institute of Systems Engineering Huazhong University of Science & Technology Wuhan 430074 China School of Economics Renmin University of China Beijing 100872 China

Credit scoring has become a critical and challenging management science issue as the credit industry has been facing stiffer competition in recent years. Many classification methods have been suggested to tackle this problem in the literature. In this paper, we investigate the performance of various credit scoring models and the corresponding credit risk cost for three real-life credit scoring data sets. Besides the well-known classification algorithms （e.g. linear discriminant analysis, logistic regression, neural networks and k-nearest neighbor）, we also investigate the suitability and performance of some recently proposed, advanced data mining techniques such as support vector machines （SVMs）, classification and regression tree （CART）, and multivariate adaptive regression splines （MARS）. The performance is assessed by using the classification accuracy and cost of credit scoring errors. The experiment results show that SVM, MARS, logistic regression and neural networks yield a very good performance. However, CART and MARS＇s explanatory capability outperforms the other methods.

关键词： Data mining credit scoring classification and regression tree support vector machines,multivariate adaptive regression splines credit-risk evaluation

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Optimal engineering system design guided by data-mining methods

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TECHNOMETRICS 2005年第3期47卷 336-348页

作者： Kim, P Ding, Y Kyungpook Natl Univ Sch Business Adm Taegu 702701 South Korea Texas A&M Univ Dept Ind Engn College Stn TX 77843 USA

An optimal engineering design problem is challenging because nonlinear objective functions usually need to be evaluated in a high-dimensional design space. This article presents a data-mining-aided optimal design method, that is able to find a competitive design solution with a relatively low computational cost. The method consists of four components: (1) a uniform-coverage selection method, that chooses design representatives from among a large number of original design alternatives for a nonrectangular design space;(2) feature functions, of which evaluation is computationally economical as the surrogate for the design objective function;(3) a clustering method, that generates a design library based on the evaluation of feature functions instead of an objective function;and (4) a classification method to create the design selection rules, eventually leading us to a competitive design. Those components are implemented to facilitate the optimal fixture layout design in a multistation panel assembly process. The benefit of the data-mining-aided optimal design is clearly demonstrated by comparison with both local optimization methods (e.g., simplex search) and random search-based optimizations (e.g., simulated annealing).

关键词： classification and regression tree fixture layout optimization K-means clustering kriging model multistation assembly processes uniform design

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Landscape-level models of potential habitat for Sonoran pronghorn

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WILDLIFE SOCIETY BULLETIN 2005年第1期33卷 24-34页

作者： O'Brien, CS Rosenstock, SS Hervert, JJ Bright, JL Boe, SR Arizona Game & Fish Dept Res Branch WMRS Phoenix AZ 85023 USA Arizona Game & Fish Dept Yuma AZ 85363 USA

A population of endangered Sonoran pronghorn (Antilocapra americana sonoriensis) exists in the United States, and 2 populations exist in Mexico. Because of the vulnerability of small, remnant populations of this subspecies to stochastic events, an important aspect of recovery planning is identifying suitable areas for establishment of new populations. To support translocation efforts, we developed landscape-level classification and regression tree (CART) and logistic regression models of potential Sonoran pronghorn habitat in southwestern Arizona through a 2-part modeling process. First, we used approximately half of Sonoran pronghorn locations (total n=3,219, collected from 1994 to 2002 from radiocollared animals in the United States) and unused points (total n=3,142, randomly generated in areas within the range of Sonoran pronghorn below 21% slope, but > 1.6 km from pronghorn locations) to create habitat models from 5 explanatory variables (i.e., slope, aspect, biome, distance to wash, and soil category). We validated models with the second half of pronghorn and unused points. Both models determined whether areas would or would not be used by Sonoran pronghorn based upon values of explanatory variables at Sonoran pronghorn locations and unused points. The CART model correctly identified 63% of pronghorn locations and 65% of unused points. The logistic regression model correctly identified 57% of pronghorn locations and 62% of unused points. Second, we created a predictive Geographic Information System (GIS) map of Sonoran pronghorn habitat and applied it to the evaluation area. Both models identified > 12,000 km(2) of potential habitat for Sonoran pronghorn on the evaluation area. Our models are a first step toward identifying potential translocation sites for Sonoran pronghorn. Potential translocation sites should be further evaluated with respect to habitat factors not included in our models, including barriers to pronghorn movements, water supplies, and forage

关键词： Antilocapra americana sonoriensis Arizona CART classification and regression tree model logistic regression Sonoran pronghorn

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A Comparative Study of Three Machine Learning Methods for Software Fault Prediction

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Journal of Shanghai Jiaotong university(Science) 2005年第2期10卷 117-121页

作者：王琪朱杰于波 Dept. of Electronic Eng. Shanghai Jiaotong Univ. Shanghai 200030 China System Verification Test Dept. Lucent Technologies Optical Networks Shanghai 200033

The contribution of this paper is comparing three popular machine learning methods for software fault prediction. They are classification tree, neural network and case-based reasoning. First, three different classifiers are built based on these three different approaches. Second, the three different classifiers utilize the same product metrics as predictor variables to identify the fault-prone components. Third, the predicting results are compared on two aspects, how good prediction capabilities these models are, and how the models support understanding a process represented by the data.

关键词： software quality prediction classification and regression tree artificial neural network case-based reasoning

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An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models

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INFORMATION & MANAGEMENT 2004年第1期42卷 61-73页

作者： Pendharkar, PC Penn State Univ Harrisburg Sch Business Adm Middletown PA 17057 USA

Software component size estimation is an important task in software project management. For a component-based approach, two steps may be used to estimate the overall size of object-oriented (OO) software: a designer uses metrics to predict the size of the software components and then utilizes the sizes to estimate the overall project size. Using OO software metrics literature, we identified factors that may affect the size of an OO software component. Using real-life data from 152 software components, we then determined the effect of the identified factors on the prediction of OO software component size. The results indicated that certain factors and the type of OO software component play a significant role in the estimate. It is shown how a regression tree data mining approach can be used to learn decision rules to guide future estimates. (C) 2004 Elsevier B.V. All rights reserved.

关键词： software engineering software size data mining forecasting classification and regression tree

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