Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.

ABSTRACT: The characterization of heart dynamics with a view to distinguish abnormal from normal behavior is an interesting topic in clinical sciences. Here we present an analysis of the Electro-cardiogram (ECG) signals from several healthy and unhealthy subjects using the framework of dynamical systems approach to multifractal analysis. Our analysis differs from the conventional nonlinear analysis in that the information contained in the amplitude variations of the signal is being extracted and quantified. The results thus obtained reveal that the attractor underlying the dynamics of the heart has multifractal structure and the variations in the resultant multifractal spectra can clearly separate healthy subjects from unhealthy ones. We use supervised machine learning approach to build a model that

SUBMITTER: Shekatkar SM 

PROVIDER: S-EPMC5680386 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature