mlsanityData to 14 Oct 2026

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

arXiv 2601.13234 ↗First posted 19 Jan 2026cs.CVcited by 1

On the map: State-space sequence models (S4, Mamba) → Physiological signal modeling

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.

Key facts. Cited by 1 paper 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.

Builds on

13 of 33 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network13 Oct
  2. 2024EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification20 Jul

Other references · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  4. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  5. 2019Deep learning-based electroencephalography analysis: a systematic reviewcited by 156
  6. 2021Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysiscited by 76
  7. 2020A guide to state-space modeling of ecological time seriescited by 9
  8. 2024Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformercited by 6
  9. 2018Epileptic Seizure Detection: A Deep Learning Approachcited by 5
  10. 2017Deep Recurrent Neural Networks for seizure detection and early seizure detection systemscited by 4
  11. 2021SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifiercited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Interventioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026CG-MambaNet: A spatiotemporal framework for cross-patient epileptic seizure prediction using CNN-GCN-Mamba-BiLSTM with event-level clinical evaluation
  2. 2025Mentality: A Mamba-based Approach towards Foundation Models for EEG
  3. 2022Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
  4. 2025FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model
  5. 2026Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks
  6. 2026A Neurosymbolic Framework for Interpretable Skeleton-Based Seizure Detection via Concept-Driven Logical Reasoning
  7. 2024BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging
  8. 2026MedMamba: Recasting Mamba for Medical Time Series Classification