Automatic optimum order selection of parametric modelling for the evaluation of abnormal intra-QRS signals in signal-averaged electrocardiograms

2005 ◽  
Vol 43 (2) ◽  
pp. 218-224 ◽  
Author(s):  
C. -C. Lin ◽  
C. -M. Chen ◽  
I. -F. Yang ◽  
T. -F. Yang
Keyword(s):  
Parametric Modelling ◽  
Order Selection ◽  
Optimum Order ◽  
Selection Of

scholarly journals What wild dogs want: habitat selection differs across life stages and orders of selection in a wide-ranging carnivore

BMC Zoology ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Helen M. K. O’Neill ◽  
Sarah M. Durant ◽  
Rosie Woodroffe
Keyword(s):  
Habitat Selection ◽  
Human Population ◽  
Life Stage ◽  
Selection Function ◽  
Order Selection ◽  
Life Stages ◽  
Population Densities ◽  
Selection For ◽  
Wild Dogs ◽  
Selection Of

Abstract Background Habitat loss is a key threat to the survival of many species. Habitat selection studies provide key information for conservation initiatives by identifying important habitat and anthropogenic characteristics influencing the distribution of threatened species in changing landscapes. However, assumptions about the homogeneity of individual choices on habitat, regardless of life stage, are likely to result in inaccurate assessment of conservation priorities. This study addresses a knowledge gap in how animals at different life stages diverge in how they select habitat and anthropogenic features, using a free-ranging population of African wild dogs living in a human-dominated landscape in Kenya as a case study. Using GPS collar data to develop resource selection function and step selection function models, this study investigated differences between second order (selection of home range across a landscape) and third order (selection of habitat within the home range) habitat selection across four life history stages when resource requirements may vary: resident-non-denning, resident-heavily-pregnant, resident-denning and dispersing. Results Wild dogs showed strong second order selection for areas with low human population densities and areas close to rivers and roads. More rugged areas were also generally selected, as were areas with lower percentage tree cover. The strength of selection for habitat variables varied significantly between life stages; for example, dispersal groups were more tolerant of higher human population densities, whereas denning and pregnant packs were least tolerant of such areas. Conclusions Habitat selection patterns varied between individuals at different life stages and at different orders of selection. These analyses showed that denning packs and dispersal groups, the two pivotal life stages which drive wild dog population dynamics, exhibited different habitat selection to resident-non-breeding packs. Dispersal groups were relatively tolerant of higher human population densities whereas denning packs preferred rugged, remote areas. Evaluating different orders of selection was important as the above trends may not be detectable at all levels of selection for all habitat characteristics. Our analyses demonstrate that when life stage information is included in analyses across different orders of selection, it improves our understanding of how animals use their landscapes, thus providing important insights to aid conservation planning.

A Tensor Approach to Model Order Selection of Multiple Sinusoids

2018 ◽  
Vol 25 (7) ◽  
pp. 1104-1108 ◽  
Author(s):  
Erhan A. Ince ◽  
Mehrab K. Allahdad ◽  
Runyi Yu
Keyword(s):  
Order Selection ◽  
Model Order Selection ◽  
Model Order ◽  
Selection Of

Automatic selection of arterial input function in DSC-MRI measurements for calculation of brain perfusion parameters using parametric modelling

2018 ◽  
Vol 13 (6) ◽  
pp. 58
Author(s):  
Seweryn Lipiński ◽  
Renata Kalicka
Keyword(s):  
Arterial Input Function ◽  
Regression Function ◽  
Input Function ◽  
Model Parameters ◽  
Parametric Modelling ◽  
Automatic Selection ◽  
Dsc Mri ◽  
Mri Measurements ◽  
Selection Of

A novel method and algorithm of automatic selection of arterial input function (AIF) is presented and its efficiency is proved using exemplary DSC-MRI measurements. The method chooses AIF devoted to a particular purpose, which is calculation of perfusion parameters with the use of parametric modelling of DSC-MRI data. The quality of medical diagnosis made on the basis of perfusion parameters depends on the quality of these parameters, which in turn is determined by the quality of the AIF signal. The proposed algorithm combines physiological requirements for AIF with mathematical criteria. The choice of parametric approach, instead of black-box modelling, allows better understanding of the investigated system functioning, as model parameters may be credited with physical interpretation. Furthermore, using multi-compartmental model of the DSC-MRI data with AIF regression function in an exponential form, gives direct, analytic results concerning the basic descriptors of AIF. The method chooses candidates for AIF on the basis of the descriptors quality. This step allows rejecting measurements which do not fulfil fundamental requirements concerning AIF from the physiological point of view. As these requirements are met, the next criterion can be adopted, that is the quality of fitting the regression function to measurements. The final step is choosing the AIF for calculating perfusion parameters with the best accuracy, which is attainable thanks to implementing the AIF devoted particularly to parametric modelling.

scholarly journals ICA model order selection of task co-activation networks

2013 ◽  
Vol 7 ◽  
Author(s):  
Kimberly L. Ray ◽  
D. Reese McKay ◽  
Peter M. Fox ◽  
Michael C. Riedel ◽  
Angela M. Uecker ◽  
...  
Keyword(s):  
Order Selection ◽  
Model Order Selection ◽  
Model Order ◽  
Co Activation ◽  
Selection Of
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