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Classification of pregnancy and labor contractions using a graph theory based analysis.

, , , , , , and . EMBC, page 2876-2879. IEEE, (2015)

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Product Offerings in Malicious Hacker Markets., , and . CoRR, (2016)Dynamic Behavior of Uterine Contractions: An Approach Based on Source Localization and Multiscale Modeling., , , , and . KSE, volume 326 of Advances in Intelligent Systems and Computing, page 527-540. Springer, (2014)Detecting labor using graph theory on connectivity matrices of uterine EMG., , , , , and . EMBC, page 2195-2198. IEEE, (2015)Nonlinear estimation of coupling and directionality between signals: Application to uterine EMG propagation., , , , and . EMBC, page 4366-4369. IEEE, (2013)Variation-based sparse source imaging in localizing uterine activity., , , and . EMBC, page 2948-2951. IEEE, (2017)Automatic monodimensional EHG contractions' segmentation., , , and . Int. Arab J. Inf. Technol., 16 (3A): 609-615 (2019)Quantitative performance analysis of four methods of evaluating signal nonlinearity: Application to uterine EMG signals., , , and . EMBC, page 1045-1048. IEEE, (2012)Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor., , , , , and . Signal, Image and Video Processing, 8 (6): 1169-1178 (2014)Classification of pregnancy and labor contractions using a graph theory based analysis., , , , , , and . EMBC, page 2876-2879. IEEE, (2015)Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence., , , , , , , , , and . CoRR, (2016)