Intelligent control of cardiac rhythms using artificial neural networks

Abstract

Cardiac rhythms are related to heart electrical activity, being an essential aspect of the cardiovascular physiology. Usually, these rhythms are represented by electrocardiograms (ECGs) that are useful to detect cardiac pathologies. This paper investigates the control of cardiac rhythms in order to induce normal rhythms from pathological responses. The strategy is based on the electrocardiograms and considers different pathologies. An intelligent controller is proposed considering the ECG as the observable variable. In order to allow the assessment of the control performance, synthetic ECGs are produced from a reduced-order mathematical model that presents close agreement with experimental measurements. The adopted model comprises a network of oscillators formed by sinoatrial node, atrioventricular node and His-Purkinje complex. Three nonlinear oscillators are employed to represent each one of these nodes that are connected by delayed couplings. The controller considers the control variable at the His-Purkinje complex. To evaluate the ability of the control law to deal with both intra- and interpatient variability, the heart model is assumed to be not available to the controller designer, being used only in the simulator to assess the control performance. The incorporation of artificial neural networks into a Lyapunov-based control scheme, however, allows the presented intelligent approach to compensate for unknown cardiac dynamics. Results show that abnormal rhythms can be avoided by applying the proposed control scheme, turning the electrocardiogram closer to the expected normal behavior and preventing critical cardiac responses.

On the heart rhythm analysis using a nonlinear dynamics perspective: analytical study and electronic simulation

“…With two unknowns u and v, an additional relationship is imposed on equation (13), which gives us equation (14). With = U u 3 and = V v .…”

“…Physiologically, normal compliance of the heart depends on the regularity of the system. References [9][10][11][12][13] have shown that the construction of synthetic ECGs can reveal a variety of responses, including normal and pathological rhythms. In a similar vein, Norbert Wiener and Arturo Rosenblueth, in their work on the mathematical formulation of the problem of impulse conduction in a network of connected excitable elements, notably in cardiac muscle, demonstrated the importance of the spatial role of depolarization waves on cardiac tissue, beyond just the SA and AV nodes, and the His bundles and Purkinje fibers [14].…”

Dynamical behavior analysis of the Van der Pol oscillator with sine nonlinearity subjected to non-sinusoidal periodic excitations by the bifurcation structures

“…These non-linear oscillators include the FitzHugh and Nagumo oscillato [11], the Hodgkin-Huxley oscillator [12], the Grudzinski-Zebrowski oscillator [13], the Hartley model [14], the Colpitts model [15], the Tchitnga oscillator [16], etc The results obtained thanks to the richness of their dynamics have contributed to major advances in many areas of research, both fundamental and engineering. [8,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”