In this paper, we are interested in the analysis of sequential data and we propose an original framework based on FCA. For that, we introduce sequential pattern structures, an original specification of pattern structures for dealing with sequential data. Sequential pattern structures are given by a subsumption operation between set of sequences, based on subsequence matching. To avoid a huge number of resulting concepts, domain knowledge projections can be applied. The original definition of projections is revised in order to operate on sequential pattern structures in a meaningful way. Based on the introduced definition, several projections of sequential pattern structures involving domain or expert knowledge are defined and discussed. This projections are evaluated on a real dataset on care trajectories where every hospitalization is described by a heterogeneous tuple with different fields. The evaluation reveals interesting concepts and justify the usage of introduced projections of sequential pattern structures. This research work provides a new and efficient extension of FCA to deal with complex data, which can be an alternative to the analysis of sequential datasets.