Clustering follow-up time-series recorded by cardiac implantable devices

Follow-up of patients treated by cardiac resynchronization therapy (CRT) is of great interest to prevent health deterioration in the postoperative period. In this purpose, data recorded in implantable devices (ID) can be informative. They are large, multivariate, evolutive with time, and then difficult to interpret. This study proposes a methodology, based on Multiple Correspondence Analysis (MCA) and fuzzy coding, to i) reduce the dimensionality of these data, ii) characterize the factorial axes by the variables recorded in the ID, iii) identify evolutions of patients in the factorial plane which are related to health deterioration. The results show that the first three axes of the MCA contain more than 90% of the total variance and are linked to the functional state of the patients. A simple clustering of the patients is performed and defines an area of the factorial plane, correlated with a degradation of patients' clinical state.