# Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata

arXiv: https://arxiv.org/abs/2308.15291 · HTML: https://mlsanity.com/paper/2308.15291/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [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)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [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 ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [The Efficiency Misnomer](https://mlsanity.com/paper/2110.12894/) (2021)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [Subject-Aware Contrastive Learning for Biosignals](https://mlsanity.com/paper/2007.04871/) (2020)
- [3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations](https://mlsanity.com/paper/2106.04452/) (2021)
- [Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals](https://mlsanity.com/paper/2109.08908/) (2021)
- [From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology](https://mlsanity.com/paper/2204.05044/) (2022)
- [Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models](https://mlsanity.com/paper/2211.07579/) (2022)
- [A Dual-scale Lead-seperated Transformer With Lead-orthogonal Attention And Meta-information For Ecg Classification](https://mlsanity.com/paper/2211.12777/) (2022)

## Built on by (most cited first, 15 of 15)

- [FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores](https://mlsanity.com/paper/2311.05908/) (2023)
- [Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care](https://mlsanity.com/paper/2407.17856/) (2024)
- [Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance](https://mlsanity.com/paper/2410.13867/) (2024)
- [Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study](https://mlsanity.com/paper/2502.19167/) (2025)
- [Scaling Representation Learning from Ubiquitous ECG with State-Space Models](https://mlsanity.com/paper/2309.15292/) (2023)
- [S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models](https://mlsanity.com/paper/2310.06715/) (2023)
- [Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals](https://mlsanity.com/paper/2310.07463/) (2023)
- [Benchmarking ECG FMs: A Reality Check Across Clinical Tasks](https://mlsanity.com/paper/2509.25095/) (2025)
- [Abnormality Prediction and Forecasting of Laboratory Values from Electrocardiogram Signals Using Multimodal Deep Learning](https://mlsanity.com/paper/2411.14886/) (2024)
- [Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care](https://mlsanity.com/paper/2312.11050/) (2023)
- [ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning](https://mlsanity.com/paper/2503.13570/) (2025)
- [Data reuse enables cost-efficient randomized trials of medical AI models](https://mlsanity.com/paper/2511.08986/) (2025)
- [A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking](https://mlsanity.com/paper/2601.06645/) (2026)
- [Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals](https://mlsanity.com/paper/2510.17406/) (2025)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)

## Abstract (arXiv)

Deep learning has emerged as the preferred modeling approach for automatic ECG analysis. In this study, we investigate three elements aimed at improving the quantitative accuracy of such systems. These components consistently enhance performance beyond the existing state-of-the-art, which is predominantly based on convolutional models. Firstly, we explore more expressive architectures by exploiting structured state space models (SSMs). These models have shown promise in capturing long-term dependencies in time series data. By incorporating SSMs into our approach, we not only achieve better performance, but also gain insights into long-standing questions in the field. Specifically, for standard diagnostic tasks, we find no advantage in using higher sampling rates such as 500Hz compared to 100Hz. Similarly, extending the input size of the model beyond 3 seconds does not lead to significant improvements. Secondly, we demonstrate that self-supervised learning using contrastive predictive coding can further improve the performance of SSMs. By leveraging self-supervision, we enable the model to learn more robust and representative features, leading to improved analysis accuracy. Lastly, we depart from synthetic benchmarking scenarios and incorporate basic demographic metadata alongside the ECG signal as input. This inclusion of patient metadata departs from the conventional practice of relying solely on the signal itself. Remarkably, this addition consistently yields positive effects on predictive performance. We firmly believe that all three components should be considered when developing next-generation ECG analysis algorithms.