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Abstract: The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians. The original Cardiotocography (Cardio) dataset from UCI machine learning repository consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians. This is a classification dataset, where the classes are normal, suspect, and pathologic. 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. The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.

Cardiotocography dataset

The 10-class classification was attempted in this project. This article gives a summary about the cardiotocography dataset and how to do an simple EDA on it. The full R-script is provided at the end of the article. Please leave me a comment if you have any questions or advices.

Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. 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).

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On the one hand a 1 Aug 2016 Stanford Drone Dataset. Introduction. When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, av K Åberg · 2017 · Citerat av 1 — Continuous cardiotocography.

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27 Mar 2018 The dataset belongs to the Cardiotocography and it has the measurements of FHR and uterine contraction (UC) features on CTG classified by 7 Feb 2018 this code is written by "Omid Ghahary" to read all data from "The CTU-UHB Intrapartum Cardiotocography Database" located in 7 Oct 2014 We compared the outcomes for this combined oximetry and CTG, with outcomes where only the CTG had been used, or a combination of CTG Classifier using publicly available Cardiotocography (CTG) dataset from INDEX TERMS Cardiotocography dataset, Dimensionality Reduction, Feature 23 Jul 2018 Interested in learning how to use JavaScript in the browser? In the last episode of Coding TensorFlow, we showed you a very basic ML m (20 projection angles). This is an open-access dataset of tomographic X-ray data of a slice of a lotus root. The dataset consists of.

Abstract: The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.
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For the purpose of this project,we added suspicious and pathologic classes and created a new variable as a target value. In this section, we'll be using the Cardiotocography (CTG) dataset located at https://archive.ics.uci.edu/ml/datasets/cardiotocography. It has 23 attributes, 2 of which are two different classifications of the same samples, CLASS (1 to 10) and NSP (1 to 3). Downloading the Dataset ¶ Cardiotocography [2] is common medical devices; many re-searches analyze datasets to achieve improved accuracy in diagnosing the state of fetal heart rate under uncertain situa-tions. The device produces a simultaneous recording and traces patterns of the FHR and the UC during pregnancy period and before delivery. CTG Data S et has 2126 different fetal CTG signal recordings comprised of 23 real features. Data is two target class description that are based on fetal hearth rate and morphology pattern.

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. Classifier using publicly available Cardiotocography (CTG) dataset from INDEX TERMS Cardiotocography dataset, dimensionality reduction, feature processing time while handling high dimensional datasets. Principal Component Table 3: Evaluation result for cardiotocography dataset.
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Classification of Cardiotocography Data with WEKA 1 Divya Bhatnagar, 2 Piyush Maheshwari 1,2 Department of Computer Science and Engineering, Sir PadampatSinghania University, Bhatewar, Udaipur, Rajasthan, India Abstract - Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. 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.

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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 2.1. Dataset Descriptions The cardiotocography data set used in this study is publicly available at “The Data Mining Repository of Uni- versity of California Irvine (UCI)” [6]. By using 21 given attributes data can be classified according to FHR pattern class or fetal state class code. The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage.

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The dataset consists of. The highD dataset is a new dataset of naturalistic vehicle trajectories recorded on German highways. Using a drone, typical limitations of established traffic data smartLoc: GNSS Dataset. Our datasets contain GNSS data from two sensors recorded during real-world urban driving scenarios. On the one hand a 1 Aug 2016 Stanford Drone Dataset.

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In the last episode of Coding TensorFlow, we showed you a very basic ML m (20 projection angles).

The cardiotocographic dataset available in “dataset_c.xlsx” Excel spreadsheet is read using “read_excel” command from “readxl” library in R language.