B. Kohler, C. Hennig, and R. Orglmeister, The principles of software QRS detection, IEEE Engineering in Medicine and Biology Magazine, vol.21, issue.1, pp.42-57, 2002.
DOI : 10.1109/51.993193

B. C. Yu, S. Liu, M. Lee, C. Y. Chen, and B. N. Chiang, A Nonlinear Digital Filter For Cardiac QRS Complex Detection, Journal of Clinical Engineering, vol.10, issue.3, pp.193-201, 1985.
DOI : 10.1097/00004669-198507000-00002

E. Soria, M. Sober, J. Calpe, J. Francisco, J. Chorro et al., Application of adaptive signal processing for determining the limits of P and T waves in an ECG, IEEE Transactions on Biomedical Engineering, vol.45, issue.8, pp.1077-1080, 1998.
DOI : 10.1109/10.704877

Y. H. Hu, W. J. Tompkins, J. L. Urrusti, and V. X. Afonso, Applications of artificial neural networks for ECG signal detection and classification, Journal of Electro- 240 cardiology, vol.26, pp.66-73, 1993.

S. S. Mehta and N. S. Lingayat, Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM, Computers in Biology and Medicine, vol.38, issue.1, pp.138-145, 2008.
DOI : 10.1016/j.compbiomed.2007.08.003

S. S. Mehta, C. R. Trivedi, and N. S. Lingayat, Identification and delineation of QRS 245 complexes in electrocardiogram using fuzzy C-means algorithm, Journal of Theoretical and Applied Information Technology, pp.609-617, 2009.

M. Merah, T. A. Abdelmalik, and B. H. Larbi, R-peaks detection based on stationary wavelet transform, Computer Methods and Programs in Biomedicine, vol.121, issue.3, pp.149-160, 2015.
DOI : 10.1016/j.cmpb.2015.06.003

S. Pal and M. Mitra, Empirical mode decomposition based ECG enhancement and QRS detection, Computers in Biology and Medicine, vol.42, issue.1, pp.83-92, 2012.
DOI : 10.1016/j.compbiomed.2011.10.012

P. Laguna, R. Jane, and R. , Automatic Detection of Wave Boundaries in Multilead ECG Signals: Validation with the CSE Database, Computers and Biomedical Research, vol.27, issue.1, pp.45-60, 1994.
DOI : 10.1006/cbmr.1994.1006

D. A. Coast, R. M. Stern, G. G. Cano, and S. A. Briller, An approach to cardiac arrhythmia analysis using hidden Markov models, IEEE Transactions on Biomedical Engineering, vol.37, issue.9, pp.255-826, 1990.
DOI : 10.1109/10.58593

N. P. Hughes, Probabilistic models for automated ECG interval analysis, 2006.

]. R. Andreao, B. Dorizzi, and J. Boudy, ECG signal analysis through hidden Markov models, IEEE Transactions on Biomedical Engineering, vol.53, issue.8, pp.1541-1549, 2006.
DOI : 10.1109/TBME.2006.877103

R. Andreao and J. Boudy, Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation, EURASIP Journal on Advances in Signal Processing, vol.37, issue.9, pp.1-6, 2006.
DOI : 10.1109/10.58593

M. Akhbari, M. B. Shamsollahi, O. Sayadi, A. A. Armoundas, and C. Jutten, ECG segmentation and fiducial point extraction using multi hidden Markov model, Computers in Biology and Medicine, vol.79, pp.21-29, 2016.
DOI : 10.1016/j.compbiomed.2016.09.004
URL : https://hal.archives-ouvertes.fr/hal-01482955

C. Lin, C. Mailhes, and J. Y. Tourneret, P-and T-wave delineation in ECG signals using a Bayesian approach and a partially collapsed Gibbs sampler, IEEE Trans- 270 action on Biomedical Engineering, vol.57, issue.12, pp.2840-2849, 2010.

C. Li, C. Zheng, and C. Tai, Detection of ECG characteristic points using wavelet transforms, IEEE Transaction on Biomedical Engineering, vol.42, issue.1, pp.21-28, 1995.

E. B. Mazomenos, D. Biswas, A. Acharyya, T. Chen, K. Maharatna et al., A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications, IEEE Journal of Biomedical and Health Informatics, vol.17, issue.2, pp.459-469, 2013.
DOI : 10.1109/TITB.2012.2231312

V. Bono, E. B. Mazomenos, T. Chen, J. A. Rosengarten, A. Acharyya et al., Development of an Automated Updated Selvester QRS Scoring System Using SWT-Based QRS Fractionation Detection and Classification, IEEE Journal of Biomedical and Health Informatics, vol.18, issue.1, pp.193-204, 2014.
DOI : 10.1109/JBHI.2013.2263311

A. Kumar and M. Singh, Ischemia detection using Isoelectric Energy Function, Computers in Biology and Medicine, vol.68, pp.76-83, 2016.
DOI : 10.1016/j.compbiomed.2015.11.002

F. Gargiulo, A. Fratini, M. Sansone, and C. Sansone, Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction, Computer Methods and Programs in Biomedicine, vol.121, issue.3, pp.127-136, 2015.
DOI : 10.1016/j.cmpb.2015.05.012

S. Barra, A. Casanova, M. Fraschini, and M. Nappi, EEG/ECG Signal Fusion Aimed at Biometric Recognition, ICIAP 2015 International Workshops, pp.35-42
DOI : 10.1007/978-3-319-23222-5_5

]. S. Barra, A. Casanova, M. Fraschini, and M. Nappi, Fusion of physiological measures for multimodal biometric systems, Multimedia Tools and Applications, vol.102, issue.6, pp.1-13, 2016.
DOI : 10.1016/j.neuroimage.2013.11.007

Z. Ghahramani and G. E. Hinton, Variational Learning for Switching State-Space Models, Neural Computation, vol.3, issue.4, 1998.
DOI : 10.1162/neco.1997.9.2.227
URL : http://www.gatsby.ucl.ac.uk/Hinton/../publications/papers/11-1999.ps.gz

]. Z. Ghahramani and G. E. Hinton, Switching state space models, Tech. rep, 1998.

]. V. Pavlovic, J. M. Rehg, T. Cham, and K. P. Murphy, A dynamic Bayesian network approach to figure tracking using learned dynamic models, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.94-101, 1999.
DOI : 10.1109/ICCV.1999.791203

. Hasegawa-johnson, Acoustic segmentation using switching state Kalman filter, IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.330-752, 2003.

Y. Wu, G. Hua, T. W. Yu, M. J. Wu, D. Black et al., Switching observation models for contour tracking in clutter, IEEE Conference on Computer Vision and Pattern Recognition Modeling and decoding motor cortical activity using a switching Kalman filter, pp.335-933, 2003.

V. Manfredi, S. Mahadevan, and J. F. Kurose, Switching kalman filters for prediction and tracking in an adaptive meteorological sensing network, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005., pp.197-206, 2005.
DOI : 10.1109/SAHCN.2005.1557075

H. Veeraraghavan, P. Schrater, and N. Papanikolopoulos, Switching Kalman filterbased approach for tracking and event detection at traffic intersections, Mediterranean Conference on Control and Automation, pp.1167-1172, 2005.
DOI : 10.1109/.2005.1467180
URL : http://www.cs.cmu.edu/~harini/isicmed05pre.pdf

J. Oster, J. Behar, O. Sayadi, S. Nemati, A. E. Johnson et al., Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters, IEEE Transactions on Biomedical Engineering, vol.62, issue.9, pp.2125-2134, 2015.
DOI : 10.1109/TBME.2015.2402236

N. Montazeri, M. B. Shamsollahi, D. Ge, and A. I. Hernandez, Switching Kalman filter based methods for apnea bradycardia detection from ECG signals, Physiological Measurement, vol.36, issue.9, pp.1763-1783, 2015.
DOI : 10.1088/0967-3334/36/9/1763
URL : https://hal.archives-ouvertes.fr/hal-01319958

P. E. Mcsharry, G. D. Clifford, L. Tarassenko, and L. A. Smith, A dynamical model for generating synthetic electrocardiogram signals, IEEE Transactions on Biomedical Engineering, vol.50, issue.3, pp.289-294, 2003.
DOI : 10.1109/TBME.2003.808805