Proceedings on Automation in Medical Engineering
Vol. 3 No. 1 (2026): Proc AUTOMED
https://doi.org/10.18416/AUTOMED.2026.2511

18th Interdisciplinary AUTOMED Symposium in Collaboration with the TC Medical Robotics, 2511

Local Explanations for Classification of Ventilation Data by Neural Networks

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Tim Bogumil (1) WEINMANN Emergency Medical Technology GmbH, Hamburg, Germany; 2) Institute for Software Systems, Hamburg University of Technology, Hamburg, Germany), Ulrike Engeln (1) WEINMANN Emergency Medical Technology GmbH, Hamburg, Germany; 2) Institute for Software Systems, Hamburg University of Technology, Hamburg, Germany), Sibylle Schupp (Institute for Software Systems, Hamburg University of Technology, Hamburg, Germany)

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Abstract

Neural 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.

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