# Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer

arXiv: https://arxiv.org/abs/2402.09424 · HTML: https://mlsanity.com/paper/2402.09424/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity](https://mlsanity.com/paper/2108.02297/) (2021)
- [Focal onset seizure prediction using convolutional networks](https://mlsanity.com/paper/1805.11576/) (2018)
- [A New Neuromorphic Computing Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2102.12773/) (2021)
- [Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction](https://mlsanity.com/paper/2102.08555/) (2021)

## Built on by (most cited first, 6 of 6)

- [Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions](https://mlsanity.com/paper/2409.02111/) (2024)
- [SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network](https://mlsanity.com/paper/2410.09998/) (2024)
- [Transformer-based EEG Decoding: A Survey](https://mlsanity.com/paper/2507.02320/) (2025)
- [Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction](https://mlsanity.com/paper/2407.14876/) (2024)
- [ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection](https://mlsanity.com/paper/2601.13234/) (2026)
- [Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks](https://mlsanity.com/paper/2607.16296/) (2026)

## Abstract (arXiv)

Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. This paper presents a neuromorphic Spiking Convolutional Transformer, named Spiking Conformer, to detect and predict epileptic seizure segments from scalped long-term electroencephalogram (EEG) recordings. We report evaluation results from the Spiking Conformer model using the Boston Children's Hospital-MIT (CHB-MIT) EEG dataset. By leveraging spike-based addition operations, the Spiking Conformer significantly reduces the classification computational cost compared to the non-spiking model. Additionally, we introduce an approximate spiking neuron layer to further reduce spike-triggered neuron updates by nearly 38% without sacrificing accuracy. Using raw EEG data as input, the proposed Spiking Conformer achieved an average sensitivity rate of 94.9% and a specificity rate of 99.3% for the seizure detection task, and 96.8%, 89.5% for the seizure prediction task, and needs >10x fewer operations compared to the non-spiking equivalent model.