Detection of some heart diseases using fractal dimension and chaos theory

Author

Sedielmaci, Ibticeme ; Bereksi Reguig, F.

fYear

2013

fDate

12-15 May 2013

Firstpage

89

Lastpage

94

Abstract

This study evaluates the changes in heart rate variability for 13 signals ECG signals taken from the MIT-BIH arrhythmia database to detect some major heart disease (APC, PVC, RBB, LBB) with fractal dimension. Fractal dimension is one of the best known parts of fractal analysis. A huge number of dimensions have been defined in various fields. We choose the regularization dimension [1] for detection and prediction of some hearts failure. Nonlinear analysis based on chaos theory and fractal analysis techniques may quantify abnormalities. This article emphasizes changes in time series applied on patients with heart disease.

Keywords

diseases; electrocardiography; fractals; APC; ECG signals; LBB; MIT-BIH arrhythmia database; PVC; RBB; chaos theory; fractal analysis techniques; fractal dimension; heart disease detection; heart rate variability; hearts failure detection; hearts failure prediction; nonlinear analysis; regularization dimension; Chaos; Diseases; Fractals; Frequency measurement; Heart rate variability; Pathology;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on

Conference_Location

Algiers

Type

conf

DOI

10.1109/WoSSPA.2013.6602342

Filename

6602342