An artificial neural network utilizing spectral entropy, Poincare plot, and Lyapunov exponent features can be used to classify ECG signals and identify heart disease abnormalities.
Artificial neural networks combined with specific feature extraction parameters can be utilized for the automatic classification of ECG signals to detect cardiac arrhythmias.
ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG for the use in specially designed instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analyzers. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper. This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.
Gautam et al. (Tue,) conducted a other in Cardiac arrhythmias and heart disease. Artificial neural network and wavelet analysis was evaluated on Identification of heart disease abnormalities. An artificial neural network utilizing spectral entropy, Poincare plot, and Lyapunov exponent features can be used to classify ECG signals and identify heart disease abnormalities.