CTG-OAS

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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

  1. [ BioMedLab ] Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method
  2. [ BioMedLab ] Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models
  3. [ BioMedLab ] BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
  4. [ Otitis Media ] Fusing fine-tuned deep features for recognizing different tympanic membranes
  5. [ BioMedLab ] Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks
  6. [ 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
  7. [ BioMedLab ] BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
  8. [ BioMedLab ] A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models
  9. [ Otitis Media ] Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks
  10. [ Otitis Media ] Convolutional neural network approach for automatic tympanic membrane detection and classification
  11. [ BioMedLab ] Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
  12. [ BioMedLab ] DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images
  13. [ BioMedLab ] Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information
  14. [ Haploid Maize Seeds ] Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach
  15. [ Haploid Maize Seeds ] Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques
  16. [ CTG-OAS ] Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
  17. [ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  18. [ CTG-OAS ] Analysis of Fetal Heart Rate Signal based on Neighborhood-based Variance Compression Method
  19. [ CTG-OAS ] The influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signals
  20. [ CTG-OAS ] Performance evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for computerized hypoxia detection and prediction
  21. [ CTG-OAS ] A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”
  22. [ CTG-OAS ] A study of artificial neural network training algorithms for classification of cardiotocography signals
  23. [ CTG-OAS ] Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
  24. [ CTG-OAS ] Using wavelet transform for cardiotocography signals classification
  25. [ CTG-OAS ] Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
  26. [ CTG-OAS ] A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses
  27. [ CTG-OAS ] Cardiotocography signals with artificial neural network and extreme learning machine
  28. [ CTG-OAS ] Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
  29. [ CTG-OAS ] Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
  30. [ CTG-OAS ] Open-access software for analysis of fetal heart rate signals
  31. [ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  32. [ CTG-OAS ] Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network
  33. [ CTG-OAS ] A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

Research Community

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Publications

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CTG-OAS
Preprocessing
CTG-OAS
Linear and Nonlinear Feature Extraction
CTG-OAS
Morphological Feature Extraction
CTG-OAS
Image-based Time-Frequnecy Analysis
CTG-OAS
Feature Selection
CTG-OAS
Classification & Performance Measurement

Please feel free to meet and join us.

This is an academic platform. Please feel free to meet and join us. A wide variety of talented members give our team the opportunity to innovate in nearly every domain of Biomedical Signal Processing, especially Cardiotocography.

The lastest publications!

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