Conclusion Générale
2. Perspectives et suggestions :
Cette thèse nous ouvre plusieurs perspectives, notamment pour les travaux futures, que l’on résume comme suit :
Proposition de nouvelles méthodes de modélisation par systèmes d’ordre fractionnaires de l’ECG et l’impédance respiratoire.
Extension de la classification proposée à d’autres types d’arythmies cardiaques et des maladies respiratoires.
Elaboration de nouvelles techniques de classification d’arythmies cardiaques et des maladies respiratoires plus performantes en utilisant les paramètres du modèle fractionnaire comme paramètres pertinents.
La validation de la méthode d’identification biométrique sur une large base de données et l’amélioration de la méthode en prenant en considération d’autres conditions d’acquisition.
Références
[1] J. Sabatier, O. P. Agrawal, J. A. Tenreiro Machado (Eds.), “Advances in Fractional Calculus: Theoretical Development and Applications in Physics and Engineering”, Springer, Dordrecht, 2007.
[2] M. Dalir, M. Bashour, “Applications of Fractional Calculus”, Applied Mathematical Sciences, Vol. 4, No. 21, p.1021-1032, 2010.
[3] C. A. Monje, Y. Q. Chen, B. M. Vinagre, D. Xue, V. Feliu (Eds.) “Fractional-Order Systems and Controls Fundamentals and Applications,’’ Springer-Verlag, England, UK, 2010.
[4] S. Das, I. Pan (Eds.), “Fractional Order Signal Processing: Introductory Concepts and Applications,” Springer, New York, 2012.
[5] R.L. Magin (Ed.), “Fractional calculus in bioengineering”, Begell House. Redding, 2006. [6] C. M. Ionescu (Ed.), “Fractional order models of the human respiratory system,” Ghent
University, Faculty of Engineering, Ghent, Belgium, 2009.
[7] F. Liu and K. Burrage, “Novel techniques in parameter estimation for fractional dynamical models arising from biological systems,” Computers & Mathematics with Applications, Vol. 62, No. 3, p. 822–833, 2011.
[8] B. West, “Fractal physiology and the fractional calculus: a perspective,” Frontiers Physiology, Vol. 1, Art. 12, 2010.
[9] T. Freeborn, “A survey of fractional-order circuit models for biology and biomedicine,” IEEE Journal on emerging and selected topics in circuits and systems, Vol. 3, p. 416– 424, 2013.
[10] R. Toledo-Hernandez, V. Rico-Ramirez, G. A. Iglesias-Silva, U. M. Diwekar, “A fractional calculus approach to the dynamic optimization of biological reactive systems. Part I: Fractional models for biological reactions,” Chemical Engineering Science, Vol. 117, p. 217–228, 2014.
[11] C. Ionescu, A. Lopes, D. Copot, J.A.T. Machado, J.H.T. Bates, “The Role of Fractional Calculus in Modeling Biological Phenomena: A review,” Communications in Nonlinear Science and Numerical Simulation, Vol. 51, p. 141–159, 2017.
[12] M. S. Billah, T. Binte Mahmud, F. S. Snigdha and M. A. Arafat, “A Novel Method to Model ECG Beats using Gaussian Functions,” Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China, October 15-17, 2011, p. 612-616.
[13] S. Karimifard, A. Ahmadian, “A robust method for diagnosis of morphological arrhythmias based on Hermitian model of higher-order statistics,” Biomedical Engineering Online, Vol. 10, Art. 22, 2011.
[14] J. Nunes, N. Amin, “Hilbert Transform-Based ECG Modeling,” Biomedical Engineering, Vol. 39, No. 3, p. 133-137, 2005.
[15] G. Dingfei, N. Srinivasan, and S. Krishnan, “Cardiac Arrhythmia Classification Using Autoregressive Modeling,” Biomedical Engineering Online, Vol. 1, Art. 5, 2002.
[16] M. L. Soria, J. P. Martínez, “An ECG Classifcation Model based on Multilead Wavelet Transform Features,” Computers in Cardiology, Durham, North Carolina, USA, Sept. 30-Oct. 3, p. 105-108, 2007.
[17] S. Jokic, V. Delic, Z. Peric, S. Krco, D. Sakac, “Efficient ECG Modeling using Polynomial Functions,” Electronics and Electrical Engineering, Vol. 110, No. 04, p. 121–124, 2011.
[18] I .Christov, G. Bortolan, “Ranking of pattern recognition parameters for premature
ventricular contraction classification by neural networks,” Physiol. Meas., p. 1281–1290, 2004.
[19] IEC 62D/60601-2-27. Particular requirements for the safety of electrocardiographic monitoring equipment (equivalent to AAMI EC 13), 1994.
[20] A. Ahmadian, S. Karimifard, “An efficient piecewise modeling of ECG signals based on Hermitian basis functions,” Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, August 22-26, 2007, p. 3180-3183.
[21] G. D. Clifford, A. Shoeb, P. E. McSharry, and B. A. Janz, “Model based filtering, compression and classification of the ECG,” Int. J. Bio-electromagnetism, Vol. 7, No. 1, p. 158-161, 2005.
[22] S. Chen, “A nonlinear trimmed moving averaging-based system with its application to real-time QRS beat classification,” Journal of Medical Engineering & Technology, Vol. 31, No. 6, p. 443−449, 2007.
[23] F. Melgani, Y. Bazi, “Detecting Premature Ventricular Contractions in ECG Signals with Gaussian Processes,” Computers in Cardiology, Vol. 35, p. 237−240, 2008.
[24] J. Lim, “Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system,” IEEE Trans. Neural Networks, Vol. 20, No. 3, p. 522– 527, 2009.
[25] A. Ebrahimzadeh, A. Khazaee, “Detection of premature ventricular contractions using MLP neural networks: A comparative study,” Measurement, Vol. 43, No. 1, p. 103– 112, 2010.
[26] C. Kamath, “ECG beat classification using features extracted from teager functions in time and frequency domains,” IET proceedings on Signal Processing, Vol. 5, No. 6, p. 575–581, 2011.
[27] M. Javadi, S. Atena, A. Sajedin, E. Reza, “Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning,” biomedical signal processing and control, Vol. 8, p. 289–296 , 2013.
[28] A. Khazaee, A. Zadeh, “ECG beat classification using particle swarm optimization and support vector machine,” Front. Comput. Sci, Vol. 8, No. 2, p. 217–231, 2014.
[29] M. Thomas, M. Kr Das, S. Ari, “Automatic ECG arrhythmia classification using dual tree complex wavelet based features,” AEU - International Journal of Electronics and Communications, Vol. 69, No. 4, p. 715–721, 2015.
[30] A. Goldberger, V. Bhargava, B. West, A. Mandel, “Nous avons mechanism of cardiac electrical stability,” the fractal hypothesis, Biophysical J., Vol. 48, No. 3, p. 525−528, 1985.
[31] H. Sun, A. Charef, “Fractal systems: a time domain approach,” Annals of biomedical Engineering, Vol. 18, No. 6, p. 597−621, 1990.
[32] Y. Ferdi, J. Herbeuval, A. Charef, B. Boucheham, “R wave detection using fractional digital differentiation,” ITBM-RBM Innovation et Technologie en Biologie et Médecine, Vol. 24, No. 5−6, p. 273−280, 2003.
[33] L. Sommacal, P. Melchior, J. Cabelguen, A. Oustaloup, A. Ijspeert, “Fractional multimodels of the gastrocnemius frog muscle,” Journal of Vibration and Control, Vol. 14, No. 9−10, p. 1415−1430, 2008.
[34] M. Talbi, A. Charef, “PVC discrimination using the QRS power spectrum and self-organizing maps,” Computer Methods and Programs in biomedicine, Vol. 94, No. 3, p. 223–231, 2009.
[35] M. Benmalek, A. Charef, “Digital fractional order operators for R-wave detection in ECG signal,” IET Signal Processing, Vol. 3, No. 5, p. 381−391, 2009.
[36] C. Ionescu, K. Desager, R. De Keyser, “Fractional order model parameters for the respiratory input impedance in healthy and in asthmatic children,” Computer Methods and Programs in biomedicine, Vol. 101, No. 3, p. 315−323, 2011.
[37] R. Magin, M. Ortigueira, I. Podlubny, J. Trujillo, “On the fractional signals and systems,” Signal Processing, Vol. 91, No. 3, p. 350−371, 2011.
[38] A. McLuckie (Ed), “Respiratory Disease and its Management,” Springer Science & Business Media, 2009.
[39] P. Ghafarian, H. Jamaati, S. M. Hashemian, “A Review on Human Respiratory Modeling,” Tanaffos Journal, Vol. 15, No. 2, p. 61-69, 2016.
[40] C.M. Ionescu, R. De Keyser, “Relations between fractional order model parameters and lung pathology in chronic obstructive pulmonary disease,” IEEE Trans. Biomed. Eng., Vol. 56, No. 4, p. 978–987, 2011.
[41] E. Marieb (Ed.), “Anatomie et physiologie humaines,” De Boeck Université, Paris, France, 1999.
[42] D. P. Zipes, J. Jalife (Ed), “Cardiac electrophysiology: from cell to bedside,” Philadelphia, W.B. Saunders and Company, 2004.
[43] S. Silbernagl, A. Despopoulos (Ed), “Atlas de poche de physiologie,” Flammarion Médecine-Sciences, 2001.
[44] A. Gacek, W. Pedrycz, (Eds.), “ECG Signal Processing, Classification and Interpretatinous avons Comprehensive Framework of Computational Intelligence,” Springer, 2012.
[45] J. A. Kastor (Ed), “Arrhythmias,” 2nd edn. Philadelphia: WB Saunders, 2000.
[46] DP, Zipes and J, Jalife (Ed), “Cardiac Electrophysiology from Cell to Bedside,” 3rd edn. Philadelphia: WB Saunders, 2000.
[47] C. Ionescu (Ed), “The human respiratory system: an analysis of the interplay between anatomy, structure, breathing and fractal dynamics,” Springer, series in Bio Engineering, Ed., Springer, London, UK, 2013.
[48] H. Maes, G. Vandersteen, M. Muehlebach, C. Ionescu, “A fan-based, low frequency, forced oscillation technique apparatus,” IEEE Transaction on instrumentation and neasurement, Vol. 63, No. 3, 2014.
[49] R. Hilfer, (Ed), “Applications of calculus in physics,’’ World Scientific, Singapore, 2000 [50] I. Petras, I. Podlubny, P. O’Leary, L. Dorcak, and B. M. Vinagre, “Analogue Realization
of Fractional Order Controllers,’’ Fakulta Berg , TU Kosice, 2002.
[51] S. Das, “Functional Fractional Calculus for System Identification and Controls,’’ Springer, New York, 2008.
[52] J. T. Machado, V. Kiryakova, F. Mainardi, “Recent history of fractional calculus,” Commun. Nonlinear Sci. Numer. Simul. Vol. 16, p. 1140–1153, 2011.
[53] I. Assadi, A. Charef, M.L. Talbi and T. Bensouici, “QRS complex frequency modeling based on Fractional order system,” Proceedings of the International Conference on Fractional Systems and Signals FSS13, Ghent, Belgium, October 24-26, 2013.
[54] C. Ionescu, C., E. Derom, R. D. Keyser, “Modelling respiratory impedance in patients with kyphoscoliosis,” Biomedical signal processing control, Vol. 11, p. 36–41, 2014. [55] I. Podlubny, “Fractional Differential Equations,” Academic Press. San Diego, 1999. [56] D.Matignon, “Stability properties for generalized fractional differential systems,” In
Proc. of the colloquium FDS’98: Fractional differential systems: Models, Methods and Applications, Vol. 5, p. 145–158, Paris, 1998.
[57] A. Charef, “Modeling and Analog Realization of the Fundamental Linear Fractional Order Differential Equation”. Nonlinear Dynamics, 46, 195-210, 2006.
[58] C. C. Tseng, “Design of fractional order digital FIR differentiators,” IEEE Signal Processing Letters, Vol. 8, No. 3, p. 77–79, 2001.
[59] Y. Q. Chen and K. L. Moore, “Discrete schemes for fractional-order differentiators and integrators,” IEEE Trans. Circuits and Systems-I, Vol. 49, No. 3, p. 363–367, 2002. [60] Y.Q. Chen and B.M. Vinagre, “A new IIR-type digital fractional order differentiator,”
Signal Processing, Vol. 83, No. 11, p. 2359–2365, 2003.
[61] Y. Q. Chen, B. M. Vinagre and I. Podlubny, “Continued fraction expansion approaches to discretizing fractional order derivatives-an expository review,” Nonlinear Dynamics, Vol. 38, No. 1–2, p. 155–170, 2004.
[62] Y. Ferdi, “Computation of Fractional Order Derivative and Integral via Power Series Expansion and Signal Modelling,” Nonlinear Dyna., Vol. 46, No. 1–2, p. 1–15, 2006. [63] K. Hamdaoui and A. Charef, “A New Discretization Method for Fractional Order
Differentiators via Bilinear Transformation,” Proc. 15th Int. Conference on Digital Signal Processing, Cardiff, UK, July 1–4, 2007, p. 280–283.
[64] J. Bates, “A recruitment model of quasi-linear power-law stress adaptation in lung tissue”, Annals of Biomedical Engineering, Vol. 35, No. 7, p. 1165–1174, 2007.
[65] N. Sebaa, Z. E. A. Fellah, W. Lauriks, C. Depollier, “Application of fractional calculus to ultrasonic wave propagation in human cancellous bone,” Signal Processing, Vol. 86, No. 10, p. 2668 – 2677, 2006.
[66] K. Assaleh; W.M. Ahmad, “Modeling of speech signals using fractional calculus,” 9th International Symposium on Signal Processing and Its Applications, ISSPA 2007. 12-15 Feb. 2007, p.1 – 4.
[67] O. Berenfeld, D. Sadeh, S. Abboud, “Simulation of late potentials using a computerized three dimensional model of the heart's ventricles with fractal conduction system,” Computers in Cardiology, 1990.
[68] M. Benmalek and A. Charef, “Digital fractional ordre operators for R-wave detection in ECG signal”, IET proceedings on Signal Processing, 2009, Vol. 3, No. 5, p. 381– 391. [69] E. Salazar, J. Knowles, “An analysis of pressure-volume characteristics of the lungs,” J
Appl Physiol, Vol. 19, p.97–104, 1964.
[70] J. Hildebrandt, “Comparison of mathematical models for cat lung and viscoelastic balloon derived by Laplace transform methods from pressure-volume data,” Bull Math Biophys Vol.31, p.651–667, 1969.
[71] J. Hildebrandt, “Pressure-volume data of cat lung interpreted by a plastoelastic, linear viscoelastic model”, Journal of Applied Physiology, Vol. 28, No. 3, p. 365–372, 1970. [72] Z. Hantos, B. Daroczy, B. Suki, S. Nagy, J. Fredberg, “Input impedance and peripheral
inhomogeneity of dog lungs,” J Appl Phys, Vol. 72, No. 1, p.168–178, 1992.
[73] B. Suki, A. Barabasi, K. Lutchen, “Lung tissue viscoelasticity: a mathematical framework and its molecular basis”, J. Appl. Physiol., Vol. 76, No. 6, p. 2749–2759, 1994.
[74] G. Losa, D. Merlini, T. Nonnenmacher, E. Weiber, “Fractals in Biology and Medicine, vol. IV”, Birkhauser Verlag, Basel, 2005.
[75] A. Oustaloup et al. “Représentation et identification par modèle non entier,” Lavoisier, 2005.
[76] J. D. Gabano, T. Poinot, H. Kanoun, “Identification of a thermal system using continuous linear parameter-varying fractional modelling,” IET : Control Theory and Applications (IET/CTA), Vol. 5, No. 7, p. 889-899, 2011.
[77] D. Sierociuk, T. Skovranek, M. Macias, I. Podlubny, I. Petras, A. Dzielinski, P. Ziubinski, “Diffusion process modelling by using fractional order models,” Applied mathematics and computation, Vol. 257, p. 2–11, 2015.
[78] D. Craiem, & R. Armentano, “A fractional derivative model to describe arterial viscoelasticity,” Biorheology, Vol. 44, p. 251–263, 2007.
[79] I. Assadi, A. Charef, T. Bensouici, N. Belgacem, “Arrhythmias discrimination based on fractional order system and KNN classifier,” 2nd international conference on intelligent signal processing ISP’15, London UK, p. 1-6, December 1-2, 2015.
[80] D. Valério, “Fractional Robust System Control,” Ph.D thesis, Instituto Superior Técnico, Universidade Técnica de Lisboa, 2005.
[81] MIT-BIH ECG Database. Online: http://www.physionet.org/physiobank/database/mitdb. [82] D. Valerio, “Ninteger v. 2.3 Fractional control toolbox for Matlab,” User and
Programmer Manual, 2005.
[83] Y. Ferdi, J. P. Herbeuval, and A. Charef, “Variance reduction of prediction error using fractional digital differentiation: application to ECG signal processing,” Proceeding of the 23rd IEEE/EMBS Annual Conf., Istanbul, Turkey, 2001, p. 25-28.
[84] C. Ionescu, R. De Keyser, J. Sabatier, A. Oustaloup, F. Levron, “Low frequency constant-phase behavior in the respiratory impedance,” Biomedical Signal Processing and Control, Vol. 6, p. 197–208, 2011.
[85] E. Oostveen, D. Macleod, H. Lorino, R. Farré, Z. Hantos, K. Desager, F. Marchal, “The forced oscillation technique in clinical practice: methodology, recommendations and future developments,” Eur. Respir. J., Vol. 22, p. 1026–1041, 2003.
[86] S. Dutta, A. Chatterjee, S. Munshi, “Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification,” Medical Engineering & Physics, Vol. 32, No. 10, p. 1161-1169, 2010.
[87] A. Daamouche, L. Hamami, N. Alajlan, F. Melgani, “A wavelet optimization approach for ECG signal classification,” Biomedical signal processing and control, Vol. 7, No. 4, p. 342–349, 2012.
[88] A. F. Khalaf, M. I. Owis, I. A. Yassine, “A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines,” Expert Systems with Applications, Vol. 42, No. 21, p. 8361-8368, 2015.
[89] F. A. Elhaj, N. Salim, A. R. Harris, T. T. Swee, T. Ahmed, “Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals,” Computer Methods and Programs in Biomedicine, Vol. 127, p. 52-63, 2016.
[90] P. De Chazel, R. Reilly, “A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features,” IEEE transactions on biomedical engineering, Vol. 53, No. 12, p. 2535–2543, 2006.
[91] A. Khazaee, “Combining SVM and PSO for PVC Detection,” International Journal of Advances in Engineering Sciences, Vol. 3, No. 4, p. 1-5, 2013.
[92] O. Inan, L. Giovangrandi, G. Kavacs, “Robust neural-network based classification of premature ventricular contractions using wavelet transform and timing interval features,” IEEE transactions on biomedical engineering, Vol. 53, No. 12, p. 2507–2515, 2006.
[93] R. Chang, C. Lin, M. Wei, K. Lin, S. Shiue-Ru Chen, “High-Precision Real-Time Premature Ventricular Contraction (PVC) Detection System Based on Wavelet Transform,” J Sign Process Syst DOI, Vol. 77, No. 3, p. 289–296, 2014.
[94] S. Dutta, A. Chatterjee, S. Munshi, “Identification of ECG beats from cross-spectrum information aided learning vector quantization,” Measurement, Vol. 44, No. 10, p. 2020–2027, 2011.
[95] O. Sayadi, M. Shamsollahi, G. Clifford, “Robust Detection of Premature Ventricular Contractions Using a Wave-Based Bayesian Framework,” IEEE Transactions on biomedical Engineering, Vol. 57, No. 2, p. 353−362, 2010.
[96] V. Krasteva, E. Jekova, “QRS Template Matching for Recognition of Ventricular Ectopic Beats,” Annals of biomedical Engineering, Vol. 35, No. 12, p. 2065−2076, 2007.
[97] I. Assadi, A. Charef, T. Bensouici, “PVC Arrhythmia classification based on QRS Complex Fractional order System Modeling,” The 8th European Nonlinear Dynamics Conference ENOC 2014, Vienna, Austria 6-11 July, 2014.
[98] I. Assadi, A. Charef, T. Bensouici, “PVC Arrhythmia Classification Based on Fractional Order System Modeling,” soumis au journal: Signal, Image and Video Processing. [99] N. Alajlan, Y. Bazi, F. Melgani, S. Malek, M. Bencherif, “Detection of premature
ventricular contraction arrhythmias in electrocardiogram signals with kernel methods,” Signal, Image and Video Processing, Vol. 8, No. 5, p. 931−942, 2014.
[100] I. Assadi, A. Charef, D. Copot, R. De Keyser, T. Bensouici, C. Ionescu, “Evaluation of respiratory parameters by means of fractional order models,” Biomedical Signal Processing and Control, Vol. 34, p 206–213, 2017.
[101] P. Barnes, “Chronic obstructive pulmonary disease”, NEJM, Vol. 343, No. 4, p. 269–280, 2000.
[102] W. Busse, & R. Lemanske, “Asthma,” New Engl J Med, Vol. 344, No. 5, p. 350–362, 2001.
[103] N. Maglaveras, T. Stamkopoulos, K. Diamantaras, C. Pappas, M. Strintzis, “ECG pattern recognition and classification using non-linear transformations and neural networks: a review,” Int J Med Inform, Vol. 52, No. 1-3, p. 191–208, 1998.
[104] A. K. Jain, A. Ross, S. and Prabhakar, “An introduction to biometric recognition,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 1, p. 4–20, 2004.
[105] J. Leroux, P. Lamadelain, B. Dorizzi, C. Guerrier, “La biométrie – Techniques et usages,” Techniques de l’ingénieur, Vol. SI1, No. H5530, p. 1-26, 2004.
[106] L. Biel, O. Petersson, L. Philipson, and P. Wide, “ECG analysis: A new approach in human identification,” IEEE Transacation on Instrument Measurement, Vol. 50, No. 3, p. 808-812, 2001.
[107] S. Jaiswal S. S. Bhadauria R. S. Jadon, “Biometric: case study,” Journal of Global Research in Computer Science, Vol. 2, No. 10, 2011.
[108] A. Bouchemha, A. Nait-Ali, N. Doghmane, “A Robust Technique to Characterize the Palm print using Radon Transform and Delaunay Triangulation,” International Journal of Computer Applications, Vol. 10, No. 10, 2010.
[109] H. Maaref, S. Lelandais-Bonadè, V. Vigneron, K. Djemal, C. Montagne, “Traitement et Analyse de Données et d'Images - Biométrie (TADIB), Evaluation AERES,” Université d‟Evry Val d‟Essonne, 2008.
[110] Y. N. Singh, S. K. Singh, “Evaluation of Electrocardiogram for Biometric Authentication,” Journal of Information Security, Vol. 3, p. 39–48, 2012.
[111] F. Porée, G. Kervio G. Carrault, “ECG biometric analysis in different physiological recording conditions,” Signal, Image and Video Processing, Vol. 10, No. 2, p. 267–276, 2016.
[112] N. Belgacem, R. Fournier, A. Nait-Ali and F. Bereksi-Reguig, “A novel biometric authentication approach using ECG and EMG signals,” Journal of Medical Engineering & Technology, Vol. 39, No. 4, 2015.
[113] K. A. Sidek, I. Khalil, “Enhancement of low sampling frequency recordings for ECG biometric matching using interpolation,” Computer methods and programs in biomedicine, Vol. 109, p. 13–25, 2013.
[114] I. Assadi, A. Charef, T. Bensouici, N. Belgacem, A. Nait-Ali, “QRS Complex Based Human Identification,” the International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia, 19 - 21 October, 2015.
Résumé
Le travail réalisé dans cette thèse présente des techniques de traitement et d’analyse du signal électro-cardiographique (ECG) ainsi que le signal respiratoire pour la classification et la discrimination des maladies cardiaques et respiratoires en se basant sur les opérateurs et les systèmes d’ordre fractionnaire. En premier lieu, la modélisation du contenu fréquentielle du QRS complexe de l’ECG par un système linéaire d’ordre fractionnaire a été présentée. Ensuite, des méthodes de classification et de discrimination des arythmies cardiaques ont été élaborées en utilisant les paramètres du model fractionnaire du contenu fréquentielle du QRS proposé comme paramètres pertinents. Une attention particulière est portée aux battements normaux, ectopiques prématurés (PVC) ainsi qu’aux blocs de branches droites et gauches (RBBB et LBBB). Quelques expériences de classification ont été présentées en utilisant la base de données MIT/BIH. Puis, la modélisation de l’impédance respiratoire et la classification des maladies respiratoires en utilisant les opérateurs et les systèmes d’ordre fractionnaire ont été aussi présentées. Les paramètres du model fractionnaire de l’impédance respiratoire ont été utilisés par un algorithme de classification pour la discrimination des personnes sains, des patients asthmatiques et des patients avec la maladie pulmonaire obstructive chronique (COPD). La méthode de classificatinous avons été validée en utilisant des donnée collectées par l’appareil de la technique d’oscillation forcée (FOT) du département de médecine respiratoire de l'hôpital de l'Université de Gand, Belgique. Les résultats obtenus pour la modélisation du contenu fréquentielle du QRS et de l’impédance respiratoire et la discrimination des maladies cardiaques et respiratoires ont été très satisfaisants.
Mots Clés
Appareil FOT ; Arythmies cardiaques ; Classification ; Complexe QRS ; Impédance respiratoire ; Maladies respiratoires ; Modélisation ; Signal ECG ; Système d'ordre fractionnaire.
Abstract
The work realized in this thesis presents processing and analysis techniques of the electrocardiographic signal (ECG) as well as the respiratory signal for the classification and discrimination of cardiac and respiratory diseases based on fractional order operators and systems. First, the frequency content of the complex QRS of the ECG modeling by a fractional order linear system was presented. After that, classification and discrimination methods of cardiac arrhythmias have been developed using the proposed complex QRS frequency content fractional order model’s parameters as pertinent parameters. A particular attention is paid to normal beats, premature ectopic (PVC) and right and left branch blocks (RBBB and LBBB). Some classification experiments have been presented using the MIT / BIH database. Then, the respiratory impedance modeling and the respiratory diseases classification using fractional order operators and systems have been also presented. The respiratory impedance model parameters have been used by a classification algorithm for the healthy subjects, asthmatic and chronic obstructive pulmonary disease (COPD) patient’s discrimination. The classification method was validated using data collected by the forced oscillation technique (FOT) at Ghent University Hospital, Belgium. The obtained results for the frequency content of the QRS and the respiratory impedance modeling and the cardiac and respiratory diseases discrimination were very satisfactory.
Key words
Cardiac Arrhythmias; Classification ; ECG signal ; FOT device; Fractional order system ; Modeling; QRS Complex ; Respiratory diseases ; Respiratory impedance.