Explainable Spike Detection Algorithm on C-fiber Microneurography Data using Weighted Hermite Variable Projection Neural Networks

Abstract

Microneurography data provides valuable insights into peripheral nerve responses critical for understanding sensory functions. This paper presents a unique approach by utilizing a neural network solution to detect spike events in microneurography data, employing the weighted Hermite Variable Projection Neural Network model to capture distinct neurophysiological patterns. Addressing the inherent challenges of low signal-to-noise ratios and extreme class imbalance, the proposed pipeline integrates a knowledge-augmented model, data balancing techniques and explainable AI methods to ensure both accurate and interpretable predictions. A sliding window technique is applied to the one-dimensional time series signal, where window-wise binary classifications are transformed back into the time series, resulting in continuous signal detection. The model's decision-making process is analyzed post-hoc, uncovering the neural patterns and feature extraction mechanisms it has discovered. This detection-focused approach is the first neural-network-based solution for spike detection in microneurography data. Source code is available at https://github.com/DominikaDarabos/explainable_MNG_spike_detection.git.Clinical relevance- Microneurography is essential for studying peripheral neural activity, but detecting spikes elicited by additional stimuli remains a significant challenge. Inaccurate detection can impact data interpretation, affecting insights into pain and itch mechanisms. The proposed spike detector introduces a fully automated solution, enhancing analysis reliability and potentially improving treatment and therapy.

Authors 8

1. Dominika Darabos

   Eötvös Loránd University

   Affiliation as printed

   Eötvös Loránd University,Budapest,Hungary

2. Alina Troglio Aachen Institute of Neurophysiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Neurophysiology RWTH Aachen University Hospital,Aachen,Germany

3. Andrea Fiebig Aachen Research group Neuroscience

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Research Group Neuroscience (IZKF),Aachen,Germany

4. Anna Maxion Aachen Institute for Computational Biomedicine

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Computational Biomedicine,Aachen,Germany

5. Ekaterina Kutafina

   University of Cologne · University Hospital Cologne

   Affiliation as printed

   University Hospital Cologne, University of Cologne,Institute for Biomedical Informatics Faculty of Medicine,Cologne,Germany

6. Barbara Namer

   Universitätsklinikum Würzburg

   Affiliation as printed

   University Hospital Würzburg,Department of Anesthesiology,Würzburg,Germany

7. Gergő Bognár

   Eötvös Loránd University

   Affiliation as printed

   Eötvös Loránd University,Department of Numerical Analysis,Budapest,Hungary

8. Péter Kovács

   Eötvös Loránd University

   Affiliation as printed

   Eötvös Loránd University,Department of Numerical Analysis,Budapest,Hungary