Evaluating atrial fibrillations through strange attractors dynamics

Nonlinear dynamics is nowadays widely employed in the study of biological phenomena. In such context, taking into account that abnormal heart rhythms display chaotic behaviours, in our opinion, the specific attractor dynamics can constitute a method for evaluating various cardiac afflictions. By using mathematical procedures specific to nonlinear dynamics we devise a new method for evaluating atrial fibrillations. Using data from ECG signals, we construct strange attractors corresponding to the phase space, specific to the analyzed signals. We show that their dynamics reflect abnormal heart rhythms. The skewness and kurtosis values are in accordance with pulse rate distributions from histograms of the analyzed signals. The Lyapunov exponent has positive values, close to zero for normal heart rhythm and with values over one order of magnitude higher in the case of fibrillation crises, highlighting a chaotic behavior for cardiac muscle dynamics. All the employed statistical parameters were calculated for a total of 5 cases (ECG signals) and statistical correlations were made using Python programming language. The presented results show that by applying nonlinear dynamics methods for analyzing the heart electrical activity we can obtain valuable information regarding fibrillation crises.