Estimation of sparse partial correlation graphs is discussed within the multivariate locally-stationary wavelet framework. We discuss the requirement for regularisation in such a framework and how this effects estimation. We observe that sparse model selection in the framework promotes more robust estimates of multivariate LSW processes, and improves interpretation through graph selection. The method is applied to study evolving correlation dynamics throughout an epileptic seizure.