Discrete event diagnosis of environment impacts

This paper is focused on the improvement of the automated environment impact diagnosis on human health. Our approach is based on environmental features (EF) selection and classification, as we know that ecologic features represent patterns of important information. We assume that diagnostic, i.e. medical one, can be improved if the pattern is comprised by most of the significant environmental features. In our study, common sequence measures were employed to determine the saliency of a wide range of applications in the area of ecology, computational biology, as well as string editing, pattern recognition, etc. We assume that an important common sequence salience measure is to find the longest common subsequence for a set of n sequences. In order to perform this hard task, we use discrete event formalism, respectively Petri nets and we propose an algorithm for reducing the size of the digraphs. An interesting application to the ECG signals measured on five patients who leave in a polluted area of Romania will demonstrate that salient input features effectively aid the medical diagnosis process.