"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):
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[4] Detection and Classification of Nonstationary Signals: Application to Uterine EMG for Prognostication of Premature Delivery
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[8] Challenges and prospects of preterm birth prediction in multiple pregnancies
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[9] Uterus Elektromyogram Sinyalleri Kullanarak Kasılmaların Tespiti Ve Erken Doğum Kestirimi
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[11] Classification Techniques Using EHG Signals for Detecting Preterm Births
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[12] Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records
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[13] Human Breathing Classification Using Electromyography Signal with Features Based on Mel-Frequency Cepstral Coefficients
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[16] Recurrent Neural Networks in Medical Data Analysis and
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[17] Research Article Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
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[18] Time-frequency analysis of electrohysterogram for classification of term and preterm birth
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[19] Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women
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[20] Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
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[21] A machine learning system for automated whole-brain seizure detection
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[22] Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals
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[23] Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models
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[24] New technique based on uterine electromyography nonlinearity for preterm delivery detection
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[25] Prediction of Preterm Deliveries from EHG Signals Using Machine Learning
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[26] Kl. Comparison between Using Linear and Non-linear Features to Classify Uterine Electromyography Signals of Term and Preterm Deliveries
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[27] Muscle activity detection from myoelectric signals based on the AR-GARCH model
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