"Classification of uterine EMG signals using supervised classification method"
written by Mohamad O. Diab, Amira El-Merhie, Nour El-Halabi, Layal Khoder,
published by Journal of Biomedical Science and Engineering, Vol.3 No.9, 2010
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor
[2] Multifractal Analysis of Electromyography Data
[3] An Automatic Method for the Segmentation and Classification of Imminent Labor Contraction from Electrohysterograms
[4] Detection and Classification of Nonstationary Signals: Application to Uterine EMG for Prognostication of Premature Delivery
[5] Unsupervised Classification of Uterine Contractions Recorded Using Electrohysterography
[6] СлоЖноСти и ПеРСПективы ПРогноЗиРования ПРеЖдевРеменных Родов ПРи многоПлодной беРеменноСти
[7] Convolutional Neural Network Application in Biomedical Signals
[8] Challenges and prospects of preterm birth prediction in multiple pregnancies
[9] Uterus Elektromyogram Sinyalleri Kullanarak Kasılmaların Tespiti Ve Erken Doğum Kestirimi
[10] Epileptic seizure detection based on expected activity measurement and Neural Network classification
[11] Classification Techniques Using EHG Signals for Detecting Preterm Births
[12] Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records
Journal of Medical Systems, 2017
[13] Human Breathing Classification Using Electromyography Signal with Features Based on Mel-Frequency Cepstral Coefficients
[14] Recurrent Neural Networks in Medical Data Analysis and Classifications
Applied Computing in Medicine and Health, 2016
[15] Elektrohisterografija prepartalno in intrapartalno pri induciranih in spontanih porodih: doktorska disertacija
[16] Recurrent Neural Networks in Medical Data Analysis and
[17] Research Article Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
[18] Time-frequency analysis of electrohysterogram for classification of term and preterm birth
[19] Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women
Neurocomputing, 2015
[20] Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
B Street - downloads.hindawi.com, 2015
[21] A machine learning system for automated whole-brain seizure detection
Applied Computing and Informatics, 2015
[22] Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals
PloS one, 2015
[23] Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models
Turkish Journal of Electrical Engineering & Computer Sciences, 2014
[24] New technique based on uterine electromyography nonlinearity for preterm delivery detection
Journal of Engineering and Technology Research, 2014
[25] Prediction of Preterm Deliveries from EHG Signals Using Machine Learning
PloS one, 2013
[26] Kl. Comparison between Using Linear and Non-linear Features to Classify Uterine Electromyography Signals of Term and Preterm Deliveries
SM NaeemJ, AF Al, MA Eldosok - ieeexplore.ieee.org, 2013
[27] Muscle activity detection from myoelectric signals based on the AR-GARCH model
Statistical Signal Processing Workshop (SSP), 2012 IEEE, 2012