Early diagnosis of irregular cardiac activity through existing tools such as Electrocardiogram and greater understanding of the underlying processes is critical for saving lives. Cardiac activity originates from a deterministic dynamical system of heart with trajectories following a linear map. Irregular cardiac activity observed in arrhythmia patients adds nonlinearities to the evolution function of the dynamical system underneath. Therefore, it is of great importance to quantitatively measure this non-linearity as a biomarker for impending cardiac diseases in patients. In this work, we formulated a novel mechanism named Neural Temporal Perturbation Field where perceived nonlinearities are modeled through deep neural network with perturbated inputs. Here, we examined the nonlinear state space by modeling the volatility of outputs for slightly adjusted inputs. We discovered that volatility characteristics clearly define a decision threshold that may be employed as a biomarker in clinical practice by applying our technique to data on normal and abnormal heart activity. Our approach resulted in the greater understanding of nonlinearity and volatility of irregular cardiac activity and as a biomarker achieved comparatively better accuracy than the state-of-the-art models.