# Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models

arXiv: https://arxiv.org/abs/2211.07579 · HTML: https://mlsanity.com/paper/2211.07579/

## Key facts

- Cited by 9 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 8 of its 21 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Placed in

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

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

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [On the Parameterization and Initialization of Diagonal State Space Models](https://mlsanity.com/paper/2206.11893/) (2022)
- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)

## Built on by (most cited first, 9 of 9)

- [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023)
- [Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery](https://mlsanity.com/paper/2305.17043/) (2023)
- [Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata](https://mlsanity.com/paper/2308.15291/) (2023)
- [BenchECG and xECG: a benchmark and baseline for ECG foundation models](https://mlsanity.com/paper/2509.10151/) (2025)
- [EchoingECG: An Electrocardiogram Cross-Modal Model for Echocardiogram Tasks](https://mlsanity.com/paper/2509.25791/) (2025)
- [Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks](https://mlsanity.com/paper/2605.02500/) (2026)
- [Challenges and Limitations of Generative AI in Synthesizing Wearable Sensor Data](https://mlsanity.com/paper/2505.14206/) (2025)
- [A Simple State Space Model Excels at Multivariate Time Series Classification](https://mlsanity.com/paper/2605.27406/) (2026)
- [Interpretable Stress Detection from ECG Signals Using Motif-Based Anomaly Analysis](https://mlsanity.com/paper/2609.22179/) (2026)

## Abstract (arXiv)

The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures. This work explores the prospects of applying the recently introduced structured state space models (SSMs) as a particularly promising approach due to its ability to capture long-term dependencies in time series. We demonstrate that this approach leads to significant improvements over the current state-of-the-art for ECG classification, which we trace back to individual pathologies. Furthermore, the model's ability to capture long-term dependencies allows to shed light on long-standing questions in the literature such as the optimal sampling rate or window size to train classification models. Interestingly, we find no evidence for using data sampled at 500Hz as opposed to 100Hz and no advantages from extending the model's input size beyond 3s. Based on this very promising first assessment, SSMs could develop into a new modeling paradigm for ECG analysis.