Design of intelligent hybrid NAR-GRNN paradigm for fractional order VDP chaotic system in cardiac pacemaker with relaxation oscillator

Managing cardiac disease and abnormal heart rate variability is a challenging problem with its psychological impact on lifesaving intervention. The research presents a novel machine learning approach to paradigm dynamic pacemaker design based on fractional order modified Van der Pol oscillator system implemented to generate rich non-sinusoidal signals for cardiac intervention and treatment. The physical framework of the novel contribution comprises Sino-Atrial (SA) and Atrio-Ventricular (AV) nodes, designed to utilize fractional order excitation signals derived from the VDP dynamic system. Hybrid paradigm is designed by integrating the dynamical nonlinear autoregressive (NAR) neural networks and generalized regression neural networks (GRNNs) to analyze the parametric fractal impulse model for cardiac muscle. The chaotic pattern of the proposed nonlinear system is explored in terms of phase portraits and Lyapunov exponents. The exceptional performance of the cutting-edge fractional order multimodal computing paradigm is validated by achieving an RMSE of 0.1E-14. The dynamic chaotic and fractal trajectory of relaxation oscillator based on its computed control parameters can provide valuable insight to design robust and efficient electronic implantable cardioverter defibrillators (ICDs) as pacemakers to stabilize cardiac impulse variability and alleviate healthcare burden as compared to traditional cardiac rehabilitation procedures.