%0 Journal Article
%T Analysis of cardiac signals using spatial filling index and time-frequency domain
%A Oliver Faust
%A Rajendra Acharya U
%A SM Krishnan
%A Lim Min
%J BioMedical Engineering OnLine
%D 2004
%I BioMed Central
%R 10.1186/1475-925x-3-30
%X This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain.This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution.Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%.Bio-signals are essentially non-stationary signals; they display a fractal like self-similarity. They may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random ¨C in the time scale. However, to (study and) pinpoint anomalies in voluminous data collected over several hours is strenuous and time consuming. Therefore, computer based analytical tools for in-depth study and classification of data over day long intervals can be very useful in diagnostics.Electrocardiography deals with the electrical activity of the heart. Monitored by placing sensors at defined positions on chest and limb extremities of the subject, electrocardiogram (ECG) is a record of the origin and propagation of the electric action potential through cardiac muscle. It is considered a representative signal of cardiac physiology, useful in diagnosing cardiac disorders. The state of cardiac health is generally reflected in the shape of ECG waveform and heart rate. It may contain important pointers to the nature of diseases afflicting the heart. However, bio-signals being non-stationary signals, this reflection may occur at random in the time scale.(That is, the disease symptoms may not show up all the time, but would manifest at certain irregular intervals during
%U http://www.biomedical-engineering-online.com/content/3/1/30