SoftED metrics introduce a soft evaluation of event detection methods in time series, incorporating fuzzy logic concepts to provide temporal tolerance in detections. However, these metrics face challenges associating detections with events, especially in cases with multiple associations between detections and events. In this work, we propose structuring this association problem within the graph theory paradigm, approaching it as a bipartite graph matching problem. For this, the Hungarian algorithm is employed to solve the association problem. The results demonstrate the effectiveness of the proposed approach, highlighting the impact of improvements in the associations between detections and events.