Key result
An intelligent controller using artificial neural networks successfully stabilized normal cardiac rhythms and avoided pathological behaviors like ventricular flutter and fibrillation in a computational model.
Why the study?
The study investigated the control of cardiac rhythms to induce normal rhythms from pathological responses using electrocardiograms across different pathologies.
An artificial neural network-based intelligent controller can theoretically stabilize normal cardiac rhythms from various arrhythmias in a computational model, suggesting potential future applications in rhythm management devices.
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.
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Lima et al. (2023) studied Cardiac arrhythmias (ventricular flutter, ventricular fibrillation, atrial flutter, atrial fibrillation). Intelligent controller using artificial neural networks vs. Conventional controller or no controller was evaluated on Stabilization of normal cardiac rhythm from pathological responses. An intelligent controller using artificial neural networks successfully stabilized normal cardiac rhythms and avoided pathological behaviors like ventricular flutter and fibrillation in a computational model.
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