๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Cluster analysis of respiratory time series

โœ Scribed by J. M. Adams; E. O. Attinger; F. M. Attinger


Book ID
104731991
Publisher
Springer-Verlag
Year
1978
Tongue
English
Weight
709 KB
Volume
28
Category
Article
ISSN
0340-1200

No coin nor oath required. For personal study only.

โœฆ Synopsis


We have investigated the respiratory control system with the hypothesis that, although many variables such as minute ventilation (VI), tidal volume (VT), breathing period (TT), inspiratory duration (TI), and expiratory duration (TE) may be observed, the controller functions more simply by manipulating only 2 or 3 of these. Thus, if tidal volume is the only independent variable, TI being determined by the "off-switch" threshold, these variables should have very similar time courses. Anesthetized dogs were subjected to CO2 breathing and carotid sinus perfusion to stimulate both chemoreceptors. The time series of the variables VI, VT, TT, TE, and TI as well as PACO2 were determined on a breath by breath basis. Derived characteristics of these time series were compared using Cluster Analysis and the latent dimensionality of respiratory control determined by Factor Analysis. The characteristics of the time series clustered into 4 groups: magnitude (of the response), speed, variability and relative change. One respiratory factor accounted for 86% of the variance for the variability characteristics, 2 factors for magnitude (91%) and relative change (85%) and 3 factors for speed (89%). The respiratory variables were analysed for each of the 4 groups of characteristics with the following results: VT and TI clustered together only for the magnitude and relative change characteristics where as TT and TE clustered closely for all four characteristics. One latent factor was associated with the [TT-TE] group and the other usually with PACO2.


๐Ÿ“œ SIMILAR VOLUMES


A multistep Unsupervised Fuzzy Clusterin
โœ M.J. Fadili; S. Ruan; D. Bloyet; B. Mazoyer ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 782 KB

A paradigm independent multistage strategy based on the Unsupervised Fuzzy Clustering Analysis (UFCA) and its potential for fMRI data analysis are presented. The influence of the fuzziness index is studied using Receiver Operating Characteristics (ROC) methodology and an interval of choice, around t

Hausdorff clustering of financial time s
โœ Nicolas Basalto; Roberto Bellotti; Francesco De Carlo; Paolo Facchi; Ester Panta ๐Ÿ“‚ Article ๐Ÿ“… 2007 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 999 KB