ctg {diversityForest} | R Documentation |
Data on automatic analysis of cardiotocograms
Description
This data set contains measurements from 2126 fetal cardiotocograms (CTGs).
The CTGs were automatically processed and the respective diagnostic features measured.
The CTGs were also classified by three expert obstetricians and a consensus classification label
assigned to each of them. This description is taken from the UC Irvine Machine
Learning Repository, where this data set was downloaded from. The outcome CLASS
is categorical with ten classes that correspond to different fetal heart rate patterns.
See the 'Details' section below for further information.
Format
A data frame with 2126 observations, 25 covariates and one 10-class outcome variable
Details
The variables are as follows:
-
b
. numeric. Start instant -
e
. numeric. End instant -
LBE
. numeric. Fetal heart rate (FHR) baseline value assessed by medical expert (beats per minute) -
LB
. numeric. FHR baseline value assessed by SisPorto (beats per minute) -
AC
. numeric. Number of accelerations per second -
FM
. numeric. Number of fetal movements per second -
UC
. numeric. Number of uterine contractions per second -
DL
. numeric. Number of light decelerations per second -
DS
. numeric. Number of severe decelerations per second -
DP
. numeric. Number of prolonged decelerations per second -
DR
. numeric. Number of repetitive decelerations per second -
ASTV
. numeric. Percentage of time with abnormal short term variability -
MSTV
. numeric. Mean value of short term variability -
ALTV
. numeric. Percentage of time with abnormal long term variability -
MLTV
. numeric. Mean value of long term variability -
Width
. numeric. Width of FHR histogram -
Min
. numeric. Minimum value of FHR histogram -
Max
. numeric. Maximum value of FHR histogram -
Nmax
. numeric. Number of histogram peaks -
Nzeros
. numeric. Number of histogram zeros -
Mode
. numeric. Mode of the histogram -
Mean
. numeric. Mean of the histogram -
Median
. numeric. Median of the histogram -
Variance
. numeric. Variance of the histogram -
Tendency
. factor. Histogram tendency (-1 for left asymmetric; 0 for symmetric; 1 for right asymmetric) -
CLASS
. factor. FHR pattern class
The classes of the outcome CLASS
are as follows:
-
A
. Calm sleep -
B
. REM sleep -
C
. Calm vigilance -
D
. Active vigilance -
SH
. Shift pattern (A or Susp with shifts) -
AD
. Accelerative/decelerative pattern (stress situation) -
DE
. Decelerative pattern (vagal stimulation) -
LD
. Largely decelerative pattern -
FS
. Flat-sinusoidal pattern (pathological state) -
SUSP
. SUSP suspect pattern
This is a pre-processed version of the "Cardiotocography" data set published
in the UC Irvine Machine Learning Repository. The raw data contained four
additional variables Date
, FileName
, SegFile
, and NSP
,
which were removed in this version of the data.
Source
UC Irvine Machine Learning Repository, link: https://archive.ics.uci.edu/dataset/193/cardiotocography/ (Accessed: 29/08/2024)
References
Ayres-de Campos, D., Bernardes, J., Garrido, A., Marques-de-Sá, J., Pereira-Leite, L. (2000). SisPorto 2.0: a program for automated analysis of cardiotocograms. J Matern Fetal Med. 9(5):311-318, <doi:10.1002/1520-6661(200009/10)9:5<311::AID-MFM12>3.0.CO;2-9>.
Dua, D., Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. https://archive.ics.uci.edu/ml/.
Examples
# Load data:
data(ctg)
# Numbers of observations per outcome class:
table(ctg$CLASS)
# Dimension of data:
dim(ctg)
# First rows of data:
head(ctg)