Classic diffusion processes fail to explain the volatility of asset's return. Many empirical findings on asset's return time series such as heavy tails, skewness and volatility clustering suggest to break down the volatility of asset's return into two components, one caused by a Brownian motion and the other one by a jump process. In this article, we propose an expansion of the stochastic volatility model of Heston by adding to the instantaneous asset prices, a jump component driven by a Hawkes process with a kernel function or memory kernel that is a Fourier transform of a probability measure. This kernel function defines the memory of the asset prices process. For instance, if it is fast decreasing, the contagion effect between asset price jumps is limited in time. Otherwise, the processes remember the history of asset price jumps for a long period. To investigate the impact of different rate of decay or type of memory, we consider four probability measures, the Laplace, the Gaussian, the Logistic and the Cauchy measures. Unlike Hawkes processes with exponential kernel we lost the Markov property but preserve the stationarity property which ensures that the unconditional expected arrival rate of jump does not explode. In absence of the Markov property, we take advantage of the Fourier transform representation to derive a closed form expression of European call option price based on characteristic functions. The numerical illustration shows that our extensions of the Heston model achieve a better fit of the Euro Stoxx 50 option data than the standard version. To conclude, we analyse the sensitivity of European call option to memory and self-excitation parameters, underlying price, volatility and jump risks.