Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.

Overview

1. School of Computer Science and Engineering, Faculty of Science, Technology and Architecure (FoSTA), Manipal University Jaipur, Jaipur, Rajasthan 303007 India
2. Department of IoT and Intelligent Systems, School of Computer Science and Engineering, Faculty of Science, Technology and Architecure (FoSTA), Manipal University Jaipur, Jaipur, Rajasthan 303007 India
3. Department of Computer and Communication Engineering, School of Computer Science and Engineering, Faculty of Science, Technology and Architecure (FoSTA), Manipal University Jaipur, Jaipur, Rajasthan 303007 India

Abstract

Epileptic seizures are short episodes of abnormal electrical activity in the brain that can cause convulsions, loss of consciousness, and other similar symptoms. Despite therapy, around 30% of patients with epilepsy continue to have seizures, emphasizing the necessity for quick and effective detection measures. Accurate seizure detection allows for prompt intervention, which dramatically improves patient safety. This study has developed EffiFormer, a hybrid Vision Transformer-CNN model for reliable seizure detection using Electroencaphelogram (EEG) spectrograms. EffiFormer combines spatial feature extraction from EfficientNet with global attention mechanisms from the Data-Efficient Image Transformer, resulting in high accuracy even with limited training data. Our four-phase pipeline processes raw EEG signals through normalization, Short-Time Fourier Transform (STFT) to create spectrograms, synthetic seizure sample generation using SMOTE, and data augmentation. This study utilized a standard 60-20-20 training-validation-testing split for effectively training our proposed architecture and validating its accuracy. Further, this study tests the technique using the CHB-MIT dataset, which is a publicly available collection of long-term scalp EEG recordings from 22 pediatric patients at Children’s Hospital Boston and covers a wide spectrum of seizure and non-seizure episodes. With an average sensitivity of 99.8% and average accuracy of 99.3%, our approach offers accurate seizure detection in various scenarios. Furthermore, explainable AI (XAI) approaches emphasize EEG regions that influence the model’s results, making the model’s decision-making process more transparent and interpretable for potential clinical review.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• physionet.org/content/chbmit, at PhysioNet; found in “Data availability”

Data availability

Dataset Link: https://physionet.org/content/chbmit/1.0.0/

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 1 funder, 27 references.

Cite

This paper

Aadi, A., Sukhija, D., Shetty, R., Shukla, P., & Dhaka, V. S. (2026). Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection. Brain informatics, 13(1), 35. https://doi.org/10.1186/s40708-026-00320-2

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