Abstract:
Chaotic analysis which works all the nonlinear dynamics ofa system has been shown to
be useful in a variety of medical applications, particularly in cardiology. Chaotic
parameters have shown potential in the identification of diseases that can be identilled
[.rom bic)mcdieal signals like electrocardiogram (ECG).
In this work. underlying cha'os in ECG signals has been analyzed using various non-
Iinear techniques. First, the ECG signal is processed through a series of steps to extract
the QRS complex. From this extracted feature, bit-to-bit interval (BBI) and
instantaneous heart rate (IHR) have been calculated. Some statistical parameters like
mean, standard deviation (SD) and eoeflicient of variation arc calculated hom BBI and
IHR time series. The nonlinear techniques like phase space portrait and central
tendency measure (CTM) have been applied to the BBI and IHR. Standard database of
MIT-BIH is used as the reference data. Twenty two records normal and abnormal
rhythms of this database arc analyzed where each record is of halr-hollr duration and
contains around (,50.000 samples.
Phase space portrait of both BBI and IHR demonstrates visible attractor with little
dispersion for healthy person's ECG and a widely dispersed plot in 2-D plane lar the
abnormal ECG. CTM is calculated far both BBI and IHR lar each ECG record of the
database. A much higher value (0.7737 x 0.0946) of CTM ror IHR is observed for
eleven patients with normal beats while that far eleven patients with abnormal rhythm
is much low (0.0833 x 0.0748) and the values are signilicantly different. CTM lar BBI
or the same eleven normal records is 0.6172 x 0.1472 and thaI for eleven abnormal
records is 0.0478 x 0.0308. These rcsults indicate that ECG can be classilied based on
chaotic modelling.
