Accelerometry data are useful in the diagnosis and treatment of mental illnesses. However, the correct application of these data depends on their quality, and in the case of mental health data, it is essential to characterize the state of euthymia in order to distinguish critical states such as depression or mania. To do this, it is necessary to delimit quality time periods in the follow-up, especially those periods in which the patient is free of seizures. In this work, an algorithm suitable for isolating quality data useful for the characterization of the state of euthymia is presented. The algorithm is based on the robustness of the classification results of the data vectors.