# ECGMamba: Towards Efficient ECG Classification with BiSSM

arXiv: https://arxiv.org/abs/2406.10098 · HTML: https://mlsanity.com/paper/2406.10098/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [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)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Detecting and interpreting myocardial infarction using fully convolutional neural networks](https://mlsanity.com/paper/1806.07385/) (2018)
- [MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification](https://mlsanity.com/paper/2306.12098/) (2023)

## Built on by (most cited first, 7 of 7)

- [MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal](https://mlsanity.com/paper/2409.18828/) (2024)
- [Atrial Fibrillation Prediction Using a Lightweight Temporal Convolutional and Selective State Space Architecture](https://mlsanity.com/paper/2508.19361/) (2025)
- [S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG](https://mlsanity.com/paper/2509.03066/) (2025)
- [Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework](https://mlsanity.com/paper/2608.18451/) (2026)
- [AF-Mamba: Efficient Long-Term Signal Modeling for Early Prediction of Atrial Fibrillation Onset](https://mlsanity.com/paper/2609.06984/) (2026)
- [DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning](https://mlsanity.com/paper/2609.11997/) (2026)
- [A Hybrid CNN--State-Space--Attention Backbone with Joint-Embedding Predictive Pretraining for 12-Lead ECG Classification](https://mlsanity.com/paper/2609.29376/) (2026)

## Abstract (arXiv)

Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit inefficiencies in the inference phase. The issue is primarily attributable to the secondary computational complexity of Transformer's self-attention mechanism. particularly when processing lengthy sequences. To address this issue, we propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. ECGMamba is based on the innovative Mamba-based block, which incorporates a range of time series modeling techniques to enhance performance while maintaining the efficiency of inference. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification, achieving competitive performance. This study not only contributes to the body of knowledge in the field of ECG classification but also provides a new research path for efficient and accurate ECG signal analysis. This is of guiding significance for the development of diagnostic models for cardiovascular diseases.