  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 obtained under
controlled conditions from several healthy and unhealthy subjects using the
framework of 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 resultant multifractal spectra can clearly
separate healthy subjects from unhealthy ones. We use supervised machine
learning approach to build a model that predicts the group label of a new
subject with very high accuracy on the basis of the multifractal parameters. By
comparing the range of scaling indices in the multifractal spectra with that of
beat replicated data from the same ECG, we show how each ECG can be checked for
abnormality for variations within itself.
