# ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection

arXiv: https://arxiv.org/abs/2601.13234 · HTML: https://mlsanity.com/paper/2601.13234/

## Key facts

- Cited by 1 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 13 of its 33 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Placed in

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

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification](https://mlsanity.com/paper/2407.20254/) (2024)
- [A guide to state-space modeling of ecological time series](https://mlsanity.com/paper/2002.02001/) (2020)
- [Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer](https://mlsanity.com/paper/2402.09424/) (2024)
- [SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network](https://mlsanity.com/paper/2410.09998/) (2024)
- [Epileptic Seizure Detection: A Deep Learning Approach](https://mlsanity.com/paper/1803.09848/) (2018)
- [Deep Recurrent Neural Networks for seizure detection and early seizure detection systems](https://mlsanity.com/paper/1706.03283/) (2017)
- [SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier](https://mlsanity.com/paper/2110.02169/) (2021)

## Built on by (most cited first, 1 of 1)

- [CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention](https://mlsanity.com/paper/2606.00074/) (2026)

## Abstract (arXiv)

Epilepsy is a chronic neurological disorder marked by recurrent seizures that can severely impact quality of life. Electroencephalography (EEG) remains the primary tool for monitoring neural activity and detecting seizures, yet automated analysis remains challenging due to the temporal complexity of EEG signals. This study introduces ConvMambaNet, a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with the Mamba Structured State Space Model (SSM) to enhance temporal feature extraction. By embedding the Mamba-SSM block within a CNN framework, the model effectively captures both spatial and long-range temporal dynamics. Evaluated on the CHB-MIT Scalp EEG dataset, ConvMambaNet achieved a 99% accuracy and demonstrated robust performance under severe class imbalance. These results underscore the model's potential for precise and efficient seizure detection, offering a viable path toward real-time, automated epilepsy monitoring in clinical environments.