« Recurrence based complex network analysis of cardiovascular variability data to predict preeclampsia, Proc. Biosignal, pp.1-4, 2010. ,
Analyzing spatial characters of the ECG signal via complex network method, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), pp.1650-1653, 2011. ,
DOI : 10.1109/BMEI.2011.6098582
From time series to complex networks: The visibility graph, Proc. Natl. Acad. Sci, pp.4972-4975, 2008. ,
DOI : 10.1103/PhysRevE.73.036127
Network analysis of human heartbeat dynamics, Applied Physics Letters, vol.96, issue.7, p.73703, 2010. ,
DOI : 10.1103/PhysRevE.72.045102
Detection and prediction of the onset of human ventricular fibrillation: An approach based on complex network theory, Physical Review E, vol.84, issue.6, p.62901, 2011. ,
DOI : 10.1126/science.1089167
Quantification of regularity in RR-interval time series using pproximate entropy, sample entropy, and multi-scale entropy, 2005. ,
Testing Stationarity With Surrogates: A Time-Frequency Approach, IEEE Transactions on Signal Processing, vol.58, issue.7, pp.3459-3470, 2010. ,
DOI : 10.1109/TSP.2010.2043971
URL : https://hal.archives-ouvertes.fr/ensl-00475929
« Horizontal visibility graphs: exact results for random time series », ArXiv10024526 Cond- Mat Physicsphysics, févr, 2010. ,
Assortative and modular networks are shaped by adaptive synchronization processes, Physical Review E, vol.86, issue.1, p.15101, 2012. ,
DOI : 10.1371/journal.pone.0012200
Enhancing neural-network performance via assortativity, Enhancing neural-network performance via assortativity, p.36114, 2011. ,
DOI : 10.1152/jn.00949.2002