Nonlinear system identification of heart rate variability via an artificial neural network model

An artificial neural network (ANN) with a back propagation algorithm employing a polynomial output function at the hidden unit was utilized for the analysis of the dynamic relationship between instantaneous lung volume (ILV), arterial blood pressure (ABP) and heart rate (HR). Due to a relationship between Volterra models and ANNs, impulse response functions describing the effects of ILV on HR and ABP on HR were obtained using a 3rd-order polynomial function neural network. Consequently, the dynamics of HR responses were well captured even for frequencies less than 0.3 Hz. Furthermore, the normalized residual mean square error (NRMSE) with this method was considerably less than that of previous linear analysis techniques. Thus, the results indicated the presence of a nonlinear dynamic relationship between ILV, ABP and HR.