Dissertation (January 08, 2026): Luiz Cláudio Lemos de Oliveira

Abstract: The discovery of motifs in biomedical time series, such as electrocardiograms (ECGs), involves identifying recurrent patterns that may contain valuable diagnostic information. Traditional methods, such as SAX, are limited by strong statistical assumptions, which are particularly inadequate for complex physiological signals. In parallel, autoencoders have demonstrated superior ability to learn nonlinear representations, but their application to motif discovery in ECG data remains unexplored, constituting a significant methodological gap. This work proposes a framework that replaces SAX discretization with neural encoding while preserving the discovery pipeline based on Shannon’s entropy and frequency of occurrence. The methodology was developed in three stages: (i) validation of the autoencoder’s reconstruction ability, (ii) training models with data from the MIT-BIH Arrhythmia Database, and (iii) systematic experimental comparison with the traditional SAX method through detection experiments, parametric sensitivity analysis, and evaluation of generalization capacity. It is concluded that replacing traditional discretization with neural encoding is feasible and provides quantitative and qualitative gains in motif discovery in ECG signals, establishing a methodological basis for developing automated biomedical signal analysis tools.