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Fuzzy back_propagation model for Ecg signals Classification

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dc.contributor.authorSebti , Bourahla/ Zerkane, Nidhal/ Encadré par Aouiche, Abdelaziz
dc.date.accessioned2025-07-15T13:51:02Z
dc.date.available2025-07-15T13:51:02Z
dc.date.issued2025-06-10
dc.description.provenanceSubmitted by Tom User (dspacest@univ-tebessa.dz) on 2025-07-15T13:51:02Z No. of bitstreams: 1 Fuzzy back_propagation model for Ecg signals Classification.pdf: 4861909 bytes, checksum: acda2d6ad4353342962b32d7a3a90826 (MD5)en
dc.description.provenanceMade available in DSpace on 2025-07-15T13:51:02Z (GMT). No. of bitstreams: 1 Fuzzy back_propagation model for Ecg signals Classification.pdf: 4861909 bytes, checksum: acda2d6ad4353342962b32d7a3a90826 (MD5) Previous issue date: 2025-06-10en
dc.description.sponsorshipThis thesis explores the application of a Fuzzy Inference System (FIS) for the classification of electrocardiogram (ECG) signals, addressing the challenges posed by their nonlinearity, variability, and susceptibility to noise. The study is structured into four chapters: First, it introduces the fundamentals of nonlinear signals, emphasizing their importance in modeling complex biological systems like the ECG. The second chapter delves into cardiac physiology and the ECG's role as a diagnostic tool, detailing its waveform components and recording methodologies. The third chapter compares traditional signal classification methods (e.g., Discrete Fourier Transform, Broida, and Strejc techniques) with artificial intelligence approaches, highlighting the advantages of fuzzy logic in handling uncertainty. Finally, the fourth chapter presents simulation results using MATLAB, demonstrating the efficacy of the proposed FIS-based method in identifying and classifying ECG signals with minimal error. The research underscores the potential of fuzzy logic for enhancing diagnostic accuracy in clinical settings, paving the way for future integration into AI-assisted healthcare tools.en_US
dc.identifier.urihttp//localhost:8080/jspui/handle/123456789/12904
dc.language.isoenen_US
dc.publisherUNIVERSITE DE ECHAHID CHEIKH LARBI TEBESSIen_US
dc.subjectElectrocardiogram (ECG), Fuzzy Inference System (FIS), Nonlinear Signals, Signal Classification, Biomedical Signal Processingen_US
dc.titleFuzzy back_propagation model for Ecg signals Classificationen_US
dc.typeThesisen_US

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