CO2Inhalation

<p>Nine physiological measures during a CO2-inhalation experiment.</p>

datasets

The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.

Kristof Meers

kcpRS

Kernel Change Point Detection on the Running Statistics

Jedelyn Cabrieto

Evelien Schat

Janne Adolf

Peter Kuppens

Francis Tuerlinckx

Eva Ceulemans

CO2Inhalation function

<p>Dataframe with 239 rows and 10 columns.
 The first column indicates the experimental phase and the last nine columns correspond to the nine physiological measures tracked during the experiment:
 Breathing volume variables (ViVol, VeVol, Vent, PiaAB), breathing duration variables (Ti,Te,Tt), heart rate (HR) and RR interval (RR) or cardiac beat interval.</p>

Format

CO2 Inhalation Data — CO2Inhalation

CO2 Inhalation Data

CO2Inhalation: CO2 Inhalation Data

Description

Usage

Arguments

Format

References

Examples