Objective:To study the value of the wearable single-lead remote monitoring device with the scatterplot in chronic disease management.Methods:dmitted into 435 residents accord with the inclusion criteria of 20 primary ...Objective:To study the value of the wearable single-lead remote monitoring device with the scatterplot in chronic disease management.Methods:dmitted into 435 residents accord with the inclusion criteria of 20 primary medical institutions of Yinchuan city,and grouped voluntarily by the implementation schemes were grouped voluntarily according to the implementation schemes.According to one of the three implementation schemes selected,the general practitioner guided the subjects to take on the wearable single-lead remote monitoring device,collecting and uploading the EEG data,then diagnosed and analyzed by the synchronously generated ECG scatterplot,finally,summarized the incidence and the categories,analyzed the differences among these three groups.Results:Among 435 subjects,there were 61 normal patients and 374 arrhythmias with the detection rate of 85.98%;and among the 1672 data collected,there were 606 normal data and 1066 arrhythmia with the detection rate of 63.76%;880 data in total 333 cases with atrial premature beat;442 data in total 215 cases with occasional ventricular premature beat;37 data of 22 cases with frequent atrial beat;65 data of 28 cases with frequent ventricular premature beat;13 data of 6 cases with atrial fibrillation;25 data of 15 cases with excitation conduction disorder;2 data of 2 cases with atrial flutter;31 data of 19 cases with ventricular tachycardia;30 data of 16 cases with conduction block;and 14 data of 8 cases with Para systolic rhythm.comparing the detection rate of arrhythmia in three groups,the difference was not statistically significant(P>0.05).Conclusion:The wearable singlelead remote monitoring device with the scatterplot has high application value in cardiovascular chronic disease management.Its effectively screening,validly diagnosing and detailed classifying are helpful to the early intervention,and the protection of the patients’lives.展开更多
Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain foc...Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.展开更多
Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interest...Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.展开更多
A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At pre...A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).展开更多
Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the chall...Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the challenge to perceive the significant temporal evolution is even greater.In this paper,we present a method to plot temporal high-dimensional data in a static scatterplot;it uses the established PCA technique to project data from multiple time-steps.The key idea is to extend each individual displacement prior to applying PCA,so as to skew the projection process,and to set a projection plane that balances the directions of temporal change and spatial variance.We present numerous examples and various visual cues to highlight the data trajectories,and demonstrate the effectiveness of the method for visualizing temporal data.展开更多
We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance o...We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance of points in multidimensional space,depending on the diagram type(parallel coordinates or a horizontal collection of scatterplots),the number of data dimensions(2,4,6,or 8),and the relative distance between points(15%,20%,or 25%).For a given reference point and two target points,we instructed participants to choose the target point that was closer to the reference point in multidimensional space.We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types,and propose corresponding hypotheses that we test using task completion time,accuracy,and gaze positions as dependent variables.Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions,however,the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions.The eye-tracking data further shows significant differences between Cartesian and parallel coordinates,as well as between different numbers of dimensions.For parallel coordinates,there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions.Using an area-of-interest(AOI)based approach,we identify different reading strategies for each diagram type:For parallel coordinates,the participants’gaze frequently jumped back and forth between pairs of axes,while axes were rarely focused on when viewing Cartesian coordinates.We further found that participants’attention is biased:toward the center of the whole plot for parallel coordinates and skewed to the center/left side for Cartesian coordinates.We anticipate that these results may support the design of more effective visualizations for multidimensional data.展开更多
应用Lorenz-RR散点图、1 h t-RR散点图及逆向分析技术,对5例复杂性心律失常患者进行分析,结果发现:借助心电散点图这项技术,可以快速判断与鉴别快速性心律失常合并缓慢性心律失常,也可以快速判断多源性室性早搏中室性并行心律的存在。...应用Lorenz-RR散点图、1 h t-RR散点图及逆向分析技术,对5例复杂性心律失常患者进行分析,结果发现:借助心电散点图这项技术,可以快速判断与鉴别快速性心律失常合并缓慢性心律失常,也可以快速判断多源性室性早搏中室性并行心律的存在。心电散点图既简化了动态心电图的分析过程,也强化了其诊断与鉴别诊断功能,使复杂性心律失常的诊断更为快捷准确。展开更多
基金The Special Project of Science and technology benefit the people of Ningxia Hui Autonomous Region(2018CMG03015)。
文摘Objective:To study the value of the wearable single-lead remote monitoring device with the scatterplot in chronic disease management.Methods:dmitted into 435 residents accord with the inclusion criteria of 20 primary medical institutions of Yinchuan city,and grouped voluntarily by the implementation schemes were grouped voluntarily according to the implementation schemes.According to one of the three implementation schemes selected,the general practitioner guided the subjects to take on the wearable single-lead remote monitoring device,collecting and uploading the EEG data,then diagnosed and analyzed by the synchronously generated ECG scatterplot,finally,summarized the incidence and the categories,analyzed the differences among these three groups.Results:Among 435 subjects,there were 61 normal patients and 374 arrhythmias with the detection rate of 85.98%;and among the 1672 data collected,there were 606 normal data and 1066 arrhythmia with the detection rate of 63.76%;880 data in total 333 cases with atrial premature beat;442 data in total 215 cases with occasional ventricular premature beat;37 data of 22 cases with frequent atrial beat;65 data of 28 cases with frequent ventricular premature beat;13 data of 6 cases with atrial fibrillation;25 data of 15 cases with excitation conduction disorder;2 data of 2 cases with atrial flutter;31 data of 19 cases with ventricular tachycardia;30 data of 16 cases with conduction block;and 14 data of 8 cases with Para systolic rhythm.comparing the detection rate of arrhythmia in three groups,the difference was not statistically significant(P>0.05).Conclusion:The wearable singlelead remote monitoring device with the scatterplot has high application value in cardiovascular chronic disease management.Its effectively screening,validly diagnosing and detailed classifying are helpful to the early intervention,and the protection of the patients’lives.
文摘Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.
文摘Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.
文摘A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).
基金the Israel Science Foundation(Grant No.2366/16 and 2472/17)。
文摘Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the challenge to perceive the significant temporal evolution is even greater.In this paper,we present a method to plot temporal high-dimensional data in a static scatterplot;it uses the established PCA technique to project data from multiple time-steps.The key idea is to extend each individual displacement prior to applying PCA,so as to skew the projection process,and to set a projection plane that balances the directions of temporal change and spatial variance.We present numerous examples and various visual cues to highlight the data trajectories,and demonstrate the effectiveness of the method for visualizing temporal data.
基金We would like to thank the Carl-Zeiss-Foundation(Carl-Zeiss-Stiftung)the German Research Foundation(DFG)for financial support within project B01 of SFB/Transregio 161.
文摘We investigate task performance and reading characteristics for scatterplots(Cartesian coordinates)and parallel coordinates.In a controlled eye-tracking study,we asked 24 participants to assess the relative distance of points in multidimensional space,depending on the diagram type(parallel coordinates or a horizontal collection of scatterplots),the number of data dimensions(2,4,6,or 8),and the relative distance between points(15%,20%,or 25%).For a given reference point and two target points,we instructed participants to choose the target point that was closer to the reference point in multidimensional space.We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types,and propose corresponding hypotheses that we test using task completion time,accuracy,and gaze positions as dependent variables.Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions,however,the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions.The eye-tracking data further shows significant differences between Cartesian and parallel coordinates,as well as between different numbers of dimensions.For parallel coordinates,there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions.Using an area-of-interest(AOI)based approach,we identify different reading strategies for each diagram type:For parallel coordinates,the participants’gaze frequently jumped back and forth between pairs of axes,while axes were rarely focused on when viewing Cartesian coordinates.We further found that participants’attention is biased:toward the center of the whole plot for parallel coordinates and skewed to the center/left side for Cartesian coordinates.We anticipate that these results may support the design of more effective visualizations for multidimensional data.
文摘应用Lorenz-RR散点图、1 h t-RR散点图及逆向分析技术,对5例复杂性心律失常患者进行分析,结果发现:借助心电散点图这项技术,可以快速判断与鉴别快速性心律失常合并缓慢性心律失常,也可以快速判断多源性室性早搏中室性并行心律的存在。心电散点图既简化了动态心电图的分析过程,也强化了其诊断与鉴别诊断功能,使复杂性心律失常的诊断更为快捷准确。
