Bogumil, TimTimBogumilEngeln, UlrikeUlrikeEngelnSchupp, SibylleSibylleSchupp2026-04-132026-04-132026-03-1418th Interdisciplinary AUTOMED & MEDROB Symposium 2026https://hdl.handle.net/11420/62537Neural networks (NNs) have great potential to improve individualization of medicine, e.g., through analysis of signals. However, they are generally not interpretable. Understanding NN decisions is crucial, especially in safety-critical domains such as medicine. This work presents a new method to provide local explanations for classifications of signals made by NNs. Our method extends the Sig-LIME explanation method from one-dimensional signals to multidimensional signals by introducing new perturbation techniques. We evaluate the proposed method on an NN that classifies the positive end-expiratory pressure (PEEP) applied by a ventilator. The evaluation shows that the generated explanations are plausible, stable and concise.en#PLACEHOLDER_PARENT_METADATA_VALUE#20261Infinite Science GmbHhttps://creativecommons.org/licenses/by/4.0/Technology::620: EngineeringConference Paper10.18416/AUTOMED.2026.2511