Detecting variabilities of ECG signals by Lyapunov exponents

An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg–Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of classification accuracies. The results confirmed that the MLPNN trained with the Levenberg–Marquardt algorithm has potential in detecting the variabilities of the ECG signals (total classification accuracy was 95.00%).