Cardiotocograhpy Analysis Web Site

Open Access Software for Cardiotocography Analysis (CTG-OAS) is developed to analyze fetal heart rate (FHR) signals. The software provides several tools to characterize the FHR signals. The features obtained from different origins, such as morphological, linear, nonlinear, time-frequency and image-based time-frequency domains are used as the inputs to classifiers. In other words, the significant analysis steps regarding the machine learning are taken into account by the software. CTG-OAS is developed for academic purposes and it will be distributed free of charge.

About Software
An Open Access Software for Cardiotocography Analysis
Detection of Acceleration and Deceleration Patterns of Fetal Heart Rate Signal
What is CTG-OAS

Functions
ctgPlotNst

Publications
A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces
Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network
Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
Open-access software for analysis of fetal heart rate signals
Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
Cardiotocography signals with artificial neural network and extreme learning machine
A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses
Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
Using wavelet transform for cardiotocography signals classification
Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
A study of artificial neural network training algorithms for classification of cardiotocography signals
A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”
Performance evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for computerized hypoxia detection and prediction
The influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signals
Analysis of Fetal Heart Rate Signal based on Neighborhood-based Variance Compression Method
Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
A systematic review of automated pre-processing, feature extraction and classification of cardiotocography

Members

Assoc. Prof. Zafer CÖMERT
Samsun University, Turkey
Web | Scholar | RG

Assoc. Prof. Fatih KOCAMAZ
İnönü University, Department of Computer Engineering, Turkey
Web | Scholar | RG

Assoc. Prof. Laura BURATTİNİ
Polytechnic University, Ancoda, Italy
Web | Scholar | RG

Opt. Dr. Sami GÜNGÖR
Medical Park Hospital, Department of Obstetrics and Gynecology, Turkey
Web | Scholar | RG

Dr. Subha VELAPPAN
Manonmaniam Sundaranar University, Department of Computer Science and Engineering, India
Web | Scholar | RG

Associate Professor Manivanna Boopathi ARUMUGAM
Bahrain Training Institute, Kingdom of Bahrain
Web | Scholar | RG

Dr. Al-yousif SHAHAD
Management and Science University, Malaysia
Web | Scholar | RG

Ph.D. Candidate Zhang YANG
Hangzhou Dianzi University, China
Web | Scholar | RG

Ph.D. Candidate Agnese SBROLLİNİ
Università Politecnica delle Marche, Italy
Web | Scholar | RG

Opt. Dr. İlter DEDE
Zeynep Kamil Education and Research Hospital, Turkey
Web | Scholar | RG

Prof. Dr. Kemal POLAT
Abant İzzetbaysal University, Turkey
Web | Scholar | RG

Dr. Ümit BUDAK
Bitlis Eren University, Turkey
Web | Scholar | RG

Prof. Dr. Abdülkadir ŞENGÜR
Fırat University, Turkey
Web | Scholar | RG

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CTG-OAS

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