Cardiotocography

Donated on 9/6/2010

The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Real

# Instances

2126

# Features

21

Dataset Information

Additional Information

2126 fetal cardiotocograms (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. Classification was both with respect to a morphologic pattern (A, B, C. ...) and to a fetal state (N, S, P). Therefore the dataset can be used either for 10-class or 3-class experiments.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
LBFeatureIntegerno
ACFeatureContinuousno
FMFeatureContinuousno
UCFeatureContinuousno
DLFeatureContinuousno
DSFeatureContinuousno
DPFeatureContinuousno
ASTVFeatureIntegerno
MSTVFeatureContinuousno
ALTVFeatureIntegerno

0 to 10 of 23

Additional Variable Information

LB - FHR baseline (beats per minute) AC - # of accelerations per second FM - # of fetal movements per second UC - # of uterine contractions per second DL - # of light decelerations per second DS - # of severe decelerations per second DP - # of prolongued decelerations per second ASTV - percentage of time with abnormal short term variability MSTV - mean value of short term variability ALTV - percentage of time with abnormal long term variability MLTV - mean value of long term variability Width - width of FHR histogram Min - minimum of FHR histogram Max - Maximum of FHR histogram Nmax - # of histogram peaks Nzeros - # of histogram zeros Mode - histogram mode Mean - histogram mean Median - histogram median Variance - histogram variance Tendency - histogram tendency CLASS - FHR pattern class code (1 to 10) NSP - fetal state class code (N=normal; S=suspect; P=pathologic)

Dataset Files

FileSize
CTG.xls1.7 MB

Papers Citing this Dataset

A Differential Privacy Mechanism Design Under Matrix-Valued Query

By Thee Chanyaswad, Alex Dytso, H. Poor, Prateek Mittal. 2018

Published in ArXiv.

MVG Mechanism: Differential Privacy under Matrix-Valued Query

By Thee Chanyaswad, Alex Dytso, H. Poor, Prateek Mittal. 2018

Published in Thee Chanyaswad, Alex Dytso, H. Vincent Poor, and Prateek Mittal. 2018. MVG Mechanism: Differential Privacy under Matrix-Valued Query. In 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS'18).

The TreeRank Tournament algorithm for multipartite ranking

By Stéphan Clémençon, Sylvain Robbiano. 2015

Published in Journal of Nonparametric Statistics.

Finding reproducible cluster partitions for the k-means algorithm

By Paulo Lisboa, Terence Etchells, Ian Jarman, Simon Chambers. 2013

Published in BMC bioinformatics.

Ranking data with ordinal labels: optimality and pairwise aggregation

By Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis. 2012

Published in Machine Learning.

0 to 5 of 5

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download (330.9 KB)
5 citations
15278 views

Creators

D. Campos

J. Bernardes

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy