Abstract Background: Machine learning in tremor time-series analysis has so far been applied to differentiate tremor disorders. Respective analytical approaches can similarly be geared to explore therapeutic interventions, which conventionally are assessed by their induced amplitude changes. This may overlook subtle signal alterations. Feature-based machine learning offers the potential to explore much more granular tremor signal changes induced by therapeutic interventions.
