arXiv (All)
open access
ECG-Mamba-V2: Architectural Refinements to a Bidirectional State Space Model for Multi-Label 12-Lead ECG Classification
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:
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Mamba-Family State-Space Model Kernels on a Programmable CGLA#1063648
- DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning#999979
- Augmenting single-lead ECG interpretation through QRS waveform decomposition and rotation#344410
- MyoCodec: A Streaming Neural Codec for Electromyography#1131888
- HAT-ECG: Hybrid autoencoder-transformer architecture for ECG arrhythmia classification.#312437
Record · ID 1035331
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive