You can access the academic resources, such as the technical notes, reports, and papers published by our research group at here. Also, several digital supplementary materials used in experiments are shared in here.

Future works

  1. Evaluation of Feature Selection Algorithms on Cardiotocography Data (Under Review)
  2. A diagnostic application model based on Sequential Forward Selection Algorithm and Naïve Bayes Classifier (Under Review)
  3. Performance Evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for Computerized Hypoxia Detection and Prediction (Accepted, In progress)
  4. The Influences of Different Window Functions and Lengths on Image-based Time-Frequency Features of Fetal Heart Rate Signals (Accepted, In progress)

Our published works are listed below. If you cannot achieve full-text of articles, please do not hesitate to contact us. 

Publications International Refereed Journal
Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals
A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals
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
Publications International Conferance and Symposium
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
Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
Using wavelet transform for cardiotocography signals classification
Fetal State Assessment Based on Cardiotocography Parameters Using PCA and AdaBoost
A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”
Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach

Research Community


The members prepare universal papers collaboratively.

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Please feel free to join us. This is a sharing platform.


The papers published by the community can be examined easily at here.


Soon, you can download CTG-OAS. We are waiting for completion of the publication process.

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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