# ECG-Mamba-V2: Architectural Refinements to a Bidirectional State Space Model for Multi-Label 12-Lead ECG Classification

arXiv: https://arxiv.org/abs/2510.13046 · HTML: https://mlsanity.com/paper/2510.13046/

## 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 0 of its 10 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Placed in

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

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

- [HexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities](https://mlsanity.com/paper/2605.17875/) (2026)

## Abstract (arXiv)

State space models offer linear-time sequence modeling and are a promising backbone for multi-label 12-lead ECG classification, but the design choices that drive their accuracy remain unclear. This letter presents ECG-Mamba-V2, a set of empirical refinements to a bidirectional Vision Mamba encoder: the class token is appended at the end of the token sequence instead of the midpoint, the forward and backward scan outputs are summed without the conventional 1/2 scaling, and dropout is applied at a uniform rate across blocks. On PhysioNet/CinC Challenge 2021, ECG-Mamba-V2 reaches 0.6494 macro AUPRC and 0.9716 macro AUROC, against 0.6100 and 0.9646 for its predecessor, while using 34% fewer parameters and delivering 38% higher throughput; it wins all 15 paired runs.