The homeML suite: shareable datasets for smart home environments2013Ingår i: Health and Technology, ISSN 2190-7188, E-ISSN 2190-7196, Vol. 3, nr 2, s.

A Public Video Dataset for Road Transportation Applications Saunier, Nicolas; Ardö, Håkan; Pilot Study of Cardiotocography Simayijiang, Zhayida; Åström 

This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and consumed time during the classification process. WEKA tool is used to analyse cardiotocography data with different algorithms (neural network, decision table, bagging, the nearest neighbour, decision stump and least square support vector machine algorithm). The dataset consists of measurements of fetal heart rate and uterine contraction as features, and the fetal state class code (1=normal, 2=suspect, 3=pathologic) as a label.

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2126 . 23 . 2010 : Geo-Magnetic field and WLAN dataset for indoor localisation from wristband and smartphone. Cardiotocography (CTG) is the most common non-invasive diagnostic technique to evaluate fetal well-being. It consists in the recording of fetal heart rate (FHR; bpm) and maternal uterine contractions. Among the main parameters characterizing FHR, baseline (BL) is fundamental to determine fetal hypoxia and distress.

Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work.

Update in: Cochrane Database Syst Rev. 2010, CD001068. [3] Hardwick J.C., Duthie S.J.: “Can cardiotocography prior to induction. of labour predict obstetric 

Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine 2019-06-01 · In this article, we analyzed Cardiotocography dataset for classification of fetal state class using Jrip, Ridor, J48, NBStar, IBk, and Kstar. Initially dataset is imbalanced. So, by applying SMOTE, dataset has balanced.

The Cardiotocography Dataset applied in this study are received from UCI Machine Learning Repository. The dataset contains 2126 observation instances with 22 attributes. In this experiment, the

UCI repository, consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on. 2 Oct 2019 This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and  Introduction: Cardiotocography (CTG) is a monitoring of fetal heart rate and uterine from intrapartum CTG recordings using a provided evaluation dataset. Fetal-Heart-Rate-using-SVM · Problem Statement: · Approach: · Dataset link: http ://archive.ics.uci.edu/ml/datasets/Cardiotocography · I have published an article on  Cardiotocography is a medical device that monitors fetal heart rate and the a simulation of Rough Neural Network in classifying cardiotocography dataset. 23 Aug 2018 SUBJECTS: Cardiotocography is a technique to record the fetal heart rate The UCI Machine Learning Repository Cardiotocography dataset  10 Apr 2020 In this paper authors used the CTG dataset from UCI Irvine Machine Learning Data Repository which contains 2126 data and each data-point is  A data set containing measurements of fetal heart rate and uterine contraction from cardiotocograms.

V Subha, D Murugan, J Rani, K Rajalakshmi, T Tirunelveli. International   6 Jan 2015 D. Ayers de Campos. Source: [original](http://www.openml.org/d/1466) - UCI Please cite: A 3-class version of Cardiotocography dataset. 10 Feb 2021 [17] simulate a machine learning classifi- cation model for classifying CTG dataset using supervised artificial neural network (ANN) and support  PACS/topics: cardiotocography, machine learning techniques, classification. 1. In this sec- tion, the data set and chosen machine learning techniques.
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Fetal cardiotocography data Cardiotocography data. Akshat Dubey • updated a year ago (Version 1) Data Tasks Code (6) Discussion (1) Activity Metadata.

Cardiotocography data uncertainty is a critical task for the classification in biomedical field.
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smartLoc: GNSS Dataset. Our datasets contain GNSS data from two sensors recorded during real-world urban driving scenarios. On the one hand a 

By using 21 given attributes data can be classified according to FHR pattern class or fetal state class code. In this study, fetal state class code is used as target The Cardiotocography dataset consisted of 23 attributes and 2126 instances. All attributes were numeric. A class attribute for the Cardiotocography dataset had 3 distinct values: Normal, Suspect, and Pathologic.