# SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network

arXiv: https://arxiv.org/abs/2410.09998 · HTML: https://mlsanity.com/paper/2410.09998/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 13 Oct 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Physiological signal modeling](https://mlsanity.com/ssm/physiological-signal-modeling/)

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

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Mamba in Speech: Towards an Alternative to Self-Attention](https://mlsanity.com/paper/2405.12609/) (2024)
- [A Survey of Mamba](https://mlsanity.com/paper/2408.01129/) (2024)
- [RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection](https://mlsanity.com/paper/2406.06086/) (2024)
- [Audio Mamba: Pretrained Audio State Space Model For Audio Tagging](https://mlsanity.com/paper/2405.13636/) (2024)
- [Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer](https://mlsanity.com/paper/2402.09424/) (2024)
- [A New Neuromorphic Computing Approach for Epileptic Seizure Prediction](https://mlsanity.com/paper/2102.12773/) (2021)

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

- [DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding](https://mlsanity.com/paper/2506.21140/) (2025)
- [MVCNet: Multi-View Contrastive Network for Motor Imagery Classification](https://mlsanity.com/paper/2502.17482/) (2025)
- [ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection](https://mlsanity.com/paper/2601.13234/) (2026)
- [PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients](https://mlsanity.com/paper/2604.16554/) (2026)
- [CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients](https://mlsanity.com/paper/2605.10111/) (2026)
- [Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks](https://mlsanity.com/paper/2607.16296/) (2026)

## Abstract (arXiv)

Epileptic seizures cause abnormal brain activity, and their unpredictability can lead to accidents, underscoring the need for long-term seizure prediction. Although seizures can be predicted by analyzing electroencephalogram (EEG) signals, existing methods often require too many electrode channels or larger models, limiting mobile usability. This paper introduces a SlimSeiz framework that utilizes adaptive channel selection with a lightweight neural network model. SlimSeiz operates in two states: the first stage selects the optimal channel set for seizure prediction using machine learning algorithms, and the second stage employs a lightweight neural network based on convolution and Mamba for prediction. On the Children's Hospital Boston-MIT (CHB-MIT) EEG dataset, SlimSeiz can reduce channels from 22 to 8 while achieving a satisfactory result of 94.8% accuracy, 95.5% sensitivity, and 94.0% specificity with only 21.2K model parameters, matching or outperforming larger models' performance. We also validate SlimSeiz on a new EEG dataset, SRH-LEI, collected from Shanghai Renji Hospital, demonstrating its effectiveness across different patients. The code and SRH-LEI dataset are available at https://github.com/guoruilu/SlimSeiz.