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Sensors
Volume 26
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10.3390/s26010237
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Method for Explainable Epileptic Seizure Detection Through Wavelet Transforms Obtained by Electroencephalogram-Based Audio Recordings

by
Paul Tavolato
1,*,
Hubert Schölnast
2,
Oliver Eigner
2,
Antonella Santone
3,
Mario Cesarelli
4,
Fabio Martinelli
5 and
Francesco Mercaldo
1,3,*
1
Faculty of Computer Science, University of Vienna, 1090 Vienna, Austria
2
Department of Computer Science and Security, St. Pölten University of Applied Sciences, 3100 Sankt Pölten, Austria
3
Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
4
Department of Engineering, University of Sannio, 82100 Benevento, Italy
5
Institute of High Performance Computing and Networking, National Research Council of Italy (CNR), 87036 Rende, Italy
*
Authors to whom correspondence should be addressed.
Sensors 2026, 26(1), 237; https://doi.org/10.3390/s26010237 (registering DOI)
Submission received: 17 October 2025 / Revised: 22 December 2025 / Accepted: 25 December 2025 / Published: 30 December 2025
(This article belongs to the Special Issue Brain Activity Monitoring and Measurement (2nd Edition))
Versions Notes

Abstract

Accurate classification of brain activity from electroencephalogram signals is essential for diagnosing neurological disorders such as epilepsy. In this paper, we propose an explainable deep learning method for epileptic seizure detection. The proposed approach converts electroencephalogram signals into audio waveforms, which are then transformed into time–frequency representations using two distinct continuous wavelet transforms, i.e., the Morlet and the Mexican Hat. These wavelet-based spectrograms effectively capture both temporal and spectral characteristics of the electroencephalogram signal data and serve as inputs to a set of convolutional neural network models with the aim to detect seizure activity. To improve model transparency, the proposed method integrates three class activation mapping techniques aimed to visualize the salient regions in the wavelet images that influence each prediction. Experimental evaluation on a real-world dataset emphasizes the efficacy of wavelet-based preprocessing in electroencephalogram signal analysis in prompt epileptic seizure detection, showing an accuracy equal to 0.922.
Keywords: epilepsy; wavelet; convolutional neural network; deep learning; explainability epilepsy; wavelet; convolutional neural network; deep learning; explainability

Share and Cite

MDPI and ACS Style

Tavolato, P.; Schölnast, H.; Eigner, O.; Santone, A.; Cesarelli, M.; Martinelli, F.; Mercaldo, F. A Method for Explainable Epileptic Seizure Detection Through Wavelet Transforms Obtained by Electroencephalogram-Based Audio Recordings. Sensors 2026, 26, 237. https://doi.org/10.3390/s26010237

AMA Style

Tavolato P, Schölnast H, Eigner O, Santone A, Cesarelli M, Martinelli F, Mercaldo F. A Method for Explainable Epileptic Seizure Detection Through Wavelet Transforms Obtained by Electroencephalogram-Based Audio Recordings. Sensors. 2026; 26(1):237. https://doi.org/10.3390/s26010237

Chicago/Turabian Style

Tavolato, Paul, Hubert Schölnast, Oliver Eigner, Antonella Santone, Mario Cesarelli, Fabio Martinelli, and Francesco Mercaldo. 2026. "A Method for Explainable Epileptic Seizure Detection Through Wavelet Transforms Obtained by Electroencephalogram-Based Audio Recordings" Sensors 26, no. 1: 237. https://doi.org/10.3390/s26010237

APA Style

Tavolato, P., Schölnast, H., Eigner, O., Santone, A., Cesarelli, M., Martinelli, F., & Mercaldo, F. (2026). A Method for Explainable Epileptic Seizure Detection Through Wavelet Transforms Obtained by Electroencephalogram-Based Audio Recordings. Sensors, 26(1), 237. https://doi.org/10.3390/s26010237

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