Abstract:
Chaotic analysis and entropy measurement has been shown to be useful in a variety of
medical applications, particularly in cardiology. Chaotic parameters like Poincare plot
indexes have shown potential in the identification of diseases, especially in the analysis of
biomedical signals like electrocardiogram (ECG).
In this work, entropy measurement, Poincare plot indexes and time domain parameters in
ECG signals have been analyzed. 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. We quantified several time domain
HRV parameters: mean IHR, variance, standard deviation of normal IHR data (SDNN)
and the square root of the mean squared difference of the successive IHR data (RMSSD).
and nonlinear techniques like approximate entropy, sample entropy, and Poincare plot
indexes have been determined from the IHR. Standard database of MIT-BIH is used as
the reference data where each ECG record contains 650000 samples. Approximate entropy
(ApEn) and sample entropy (SampEn) are calculated for IHR for each ECG record of the
database. A much higher value of ApEn and SampEn for IHR is observed for eight
patients with abnormal beats like T, AFIB, VT. On the contrary, the ApEn and SampEn
for IHR of eight patients with normal rhythm shows lower value. The IHR time series
and their corresponding Poincare plots taken from the HRV patterns of patients with
normal heart beat and abnormal heart beat are presented in Figure. Poincare plot indexes
for IHR of eight normal rhythm records show lower values with a SDI of 5.6087 and SD2
of 7.I364.0n the other hand Poincare plot indexes for IHR of eight abnormal rhythm
records show higher values with a SDI of 23.2093 and SD2 of 22.6107. Time domain
parameter are found lower for eight normal rhythm records with variance of 52.6808, SD
of 6.5875, SDSD of 7.9260 and SMSSD of 77.5388 .. Time domain parameter are found
lower for eight abnormal rhythm records with variance of 562.3727, SD of 23.2972,
SDSD of 32.8103 and SMSSD of 86.6614. These results indicate that ECG can be
classified based on this chaotic modelling which works on the nonlinear dynamics of the
system.
