# Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals

arXiv: https://arxiv.org/abs/2305.04325 · HTML: https://mlsanity.com/paper/2305.04325/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Escaping the Big Data Paradigm with Compact Transformers](https://mlsanity.com/paper/2104.05704/) (2021)
- [Scaling Neural Machine Translation](https://mlsanity.com/paper/1806.00187/) (2018)
- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [Epileptic Seizures Detection Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2007.01276/) (2020)
- [EEG based Continuous Speech Recognition using Transformers](https://mlsanity.com/paper/2001.00501/) (2019)
- [ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges](https://mlsanity.com/paper/2304.14919/) (2023)

## Built on by (most cited first, 2 of 2)

- [Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment](https://mlsanity.com/paper/2412.17842/) (2024)
- [ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic Seizures](https://mlsanity.com/paper/2403.03276/) (2024)

## Abstract (arXiv)

Background: Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizure frequency and severity in order to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient reports which may cause significant issues owing to inadequate or inaccurate descriptions of seizures and their frequencies. Methods and materials: This study proposes a novel deep learning architecture based lightweight convolution transformer (LCT). The transformer is able to learn spatial and temporal correlated information simultaneously from the multi-channel electroencephalogram (EEG) signal to detect seizures at smaller segment lengths. In the proposed model, the lack of translation equivariance and localization of ViT is reduced using convolution tokenization, and rich information from the transformer encoder is extracted by sequence pooling instead of the learnable class token. Results: Extensive experimental results demonstrate that the proposed model of cross-patient learning can effectively detect seizures from the raw EEG signals. The accuracy and F1-score of seizure detection in the cross-patient case on the CHB-MIT dataset are shown to be 96.31% and 96.32%, respectively, at 0.5 sec segment length. In addition, the performance metrics show that the inclusion of inductive biases and attention-based pooling in the model enhances the performance and reduces the number of transformer encoder layers, which significantly reduces the computational complexity. In this research work, we provided a novel approach to enhance efficiency and simplify the architecture for multi-channel automated seizure detection.