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Abstract
In this paper, the analysis of the electrocardiogram (ECG) signal is carried out according a non-linear approach. This concerns the eventual fractal behavior of such signal and the correlation of such behavior with normal and pathological ECG signals. The analysis is carried out on different ECG signals taken from the MIT-BIH arrhythmia database. In fact these signals are those of six subjects with different ages and presenting both normal and abnormal arrhythmias situations. The abnormal situations are atrial premature beat (APB), premature ventricular contraction (PVC), right bundle branch block (RBBB) and left bundle branch block (LBBB). The fractal behavior of these signals is analyzed according to the determination of the multifractal spectrum and the fractal dimension variations and looking for eventually a fractal signature of each heart disease and age of the subject. The obtained results show a fractal signature according to the age and the pathologies for the studied cases. However further investigations are required on larger databases to confirm such results.
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Affiliation(s)
- IBTICEME SEDJELMACI
- Biomedical Engineering Laboratory, Electric and Electronic Engineering Department, ABBT University, Tlemcen, Algeria
| | - F. BEREKSI-REGUIG
- Biomedical Engineering Laboratory, Electric and Electronic Engineering Department, ABBT University, Tlemcen, Algeria
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Classification des stades de sommeil par des réseaux de neurones artificiels hiérarchiques. Ing Rech Biomed 2012. [DOI: 10.1016/j.irbm.2011.12.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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