Classification of uterine EMG signals using supervised classification method

Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
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"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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[1]	Electrodes in external electrohysterography: a systematic literature review
	2021

[2]	Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
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[3]	Quantitative electroencephalography as a tool for objective neurophysiological seizure diagnosis: A retrospective study
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[4]	An Automatic Method for the Segmentation and Classification of Imminent Labor Contraction from Electrohysterograms
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[5]	Detection and Classification of Nonstationary Signals: Application to Uterine EMG for Prognostication of Premature Delivery
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[6]	Unsupervised Classification of Uterine Contractions Recorded Using Electrohysterography
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[7]	Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor
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[8]	Multifractal Analysis of Electromyography Data
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[9]	СлоЖноСти и ПеРСПективы ПРогноЗиРования ПРеЖдевРеменных Родов ПРи многоПлодной беРеменноСти
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[10]	Convolutional Neural Network Application in Biomedical Signals
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[11]	Challenges and prospects of preterm birth prediction in multiple pregnancies
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[12]	Uterus Elektromyogram Sinyalleri Kullanarak Kasılmaların Tespiti Ve Erken Doğum Kestirimi
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[13]	Epileptic seizure detection based on expected activity measurement and Neural Network classification
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[14]	Classification Techniques Using EHG Signals for Detecting Preterm Births
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[15]	Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records
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[16]	Human Breathing Classification Using Electromyography Signal with Features Based on Mel-Frequency Cepstral Coefficients
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[17]	Recurrent Neural Networks in Medical Data Analysis and Classifications
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[18]	Elektrohisterografija prepartalno in intrapartalno pri induciranih in spontanih porodih: doktorska disertacija
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[19]	Recurrent Neural Networks in Medical Data Analysis and
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[20]	Research Article Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
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[21]	Time-frequency analysis of electrohysterogram for classification of term and preterm birth
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[22]	Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women
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[23]	Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
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[24]	A machine learning system for automated whole-brain seizure detection
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[25]	Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals
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[26]	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

[27]	New technique based on uterine electromyography nonlinearity for preterm delivery detection
	Journal of Engineering and Technology Research, 2014

[28]	Prediction of Preterm Deliveries from EHG Signals Using Machine Learning
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[29]	Kl. Comparison between Using Linear and Non-linear Features to Classify Uterine Electromyography Signals of Term and Preterm Deliveries
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[30]	Muscle activity detection from myoelectric signals based on the AR-GARCH model
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[1]	Electrodes in external electrohysterography: a systematic literature review Biophysical Reviews, 2021 DOI:10.1007/s12551-021-00805-w

[2]	Quantitative electroencephalography as a tool for objective neurophysiological seizure diagnosis: A retrospective study Indian Journal of Medical Sciences, 2020 DOI:10.25259/IJMS_160_2020

[3]	Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications Open Engineering, 2020 DOI:10.1515/eng-2021-0009

[4]	Quantitative electroencephalography as a tool for objective neurophysiological seizure diagnosis: A retrospective study Indian Journal of Medical Sciences, 2020 DOI:10.25259/IJMS_160_2020

[5]	An Automatic Method for the Segmentation and Classification of Imminent Labor Contraction From Electrohysterograms IEEE Transactions on Biomedical Engineering, 2020 DOI:10.1109/TBME.2019.2930618

[6]	Multifractals and Chronic Diseases of the Central Nervous System 2019 DOI:10.1007/978-981-13-3552-5_4

[7]	Detection and Classification of Nonstationary Signals: Application to Uterine EMG for Prognostication of Premature Delivery Neurophysiology, 2019 DOI:10.1007/s11062-019-09821-9

[8]	Epileptic seizure detection based on expected activity measurement and Neural Network classification 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017 DOI:10.1109/EMBC.2017.8037442

[9]	Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records Journal of Medical Systems, 2017 DOI:10.1007/s10916-017-0847-8

[10]	A machine learning system for automated whole-brain seizure detection Applied Computing and Informatics, 2016 DOI:10.1016/j.aci.2015.01.001

[11]	Applied Computing in Medicine and Health 2016 DOI:10.1016/B978-0-12-803468-2.00007-2

[12]	Time-Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Birth IEIE Transactions on Smart Processing and Computing, 2015 DOI:10.5573/IEIESPC.2015.4.2.103

[13]	Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women Neurocomputing, 2015 DOI:10.1016/j.neucom.2014.03.087

[14]	Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques BioMed Research International, 2015 DOI:10.1155/2015/986736

[15]	Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals PLOS ONE, 2015 DOI:10.1371/journal.pone.0132116

[16]	Prediction of Preterm Deliveries from EHG Signals Using Machine Learning PLoS ONE, 2013 DOI:10.1371/journal.pone.0077154

[17]	Muscle activity detection from myoelectric signals based on the AR-GARCH model 2012 IEEE Statistical Signal Processing Workshop (SSP), 2012 DOI:10.1109/SSP.2012.6319721

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