Motif identification in vital signs of chronic patients

Abstract

With the development of remote medicine, more and more programs are being opened for the remote monitoring of chronic patients. The purpose of this research is to provide maximum understanding of the patient’s condition using their vital signs collected remotely. The vital signs are presented as irregular time series. Firstly, five missing value estimation methods are used to fill gaps in data. One of the best methods is to predict missing values with an artificial neural network trained on available data. Then, we use the exact method based on the matrix profile to search for motifs in one-dimensional and two-dimensional time series. The obtained motifs can be used to monitor the patient’s condition, to assess the effectiveness of therapy or to assess the physician’s actions.