Cardiotocograhpy Analysis Web Site

CTG-OAS

CTG-OAS is an open-access software for analyzing cardiotocography (CTG) signals. The software is developed via Matlab. The main aim of this software is to ensure a computational platform for research purpose. The significant processes such as preprocessing, feature transform and classification in terms of the automated CTG analysis have been embedded into the software to develop new algorithms.

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Lastest Publication

[ CTG-OAS ] Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
[ BioMedLab ] COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
[ BioMedLab ] A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model
[ BioMedLab ] Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method
[ BioMedLab ] Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models
[ BioMedLab ] BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
[ Otitis Media ] Fusing fine-tuned deep features for recognizing different tympanic membranes
[ BioMedLab ] Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks
[ BioMedLab ] Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders
[ BioMedLab ] BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
[ BioMedLab ] A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models
[ Otitis Media ] Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks
[ Otitis Media ] Convolutional neural network approach for automatic tympanic membrane detection and classification
[ BioMedLab ] Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
[ BioMedLab ] DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images
[ BioMedLab ] Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information
[ Haploid Maize Seeds ] Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach
[ Haploid Maize Seeds ] Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques
[ CTG-OAS ] Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
[ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
[ CTG-OAS ] Analysis of Fetal Heart Rate Signal based on Neighborhood-based Variance Compression Method
[ CTG-OAS ] The influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signals
[ CTG-OAS ] Performance evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for computerized hypoxia detection and prediction
[ CTG-OAS ] A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”
[ CTG-OAS ] A study of artificial neural network training algorithms for classification of cardiotocography signals
[ CTG-OAS ] Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
[ CTG-OAS ] Using wavelet transform for cardiotocography signals classification
[ CTG-OAS ] Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
[ CTG-OAS ] A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses
[ CTG-OAS ] Cardiotocography signals with artificial neural network and extreme learning machine
[ CTG-OAS ] Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
[ CTG-OAS ] Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
[ CTG-OAS ] Open-access software for analysis of fetal heart rate signals
[ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
[ CTG-OAS ] Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network
[ CTG-OAS ] A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

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