# xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM

arXiv: https://arxiv.org/abs/2504.16101 · HTML: https://mlsanity.com/paper/2504.16101/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [xLSTM: Extended Long Short-Term Memory](https://mlsanity.com/paper/2405.04517/) (2024)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Frozen Language Model Helps ECG Zero-Shot Learning](https://mlsanity.com/paper/2303.12311/) (2023)
- [ETP: Learning Transferable ECG Representations via ECG-Text Pre-training](https://mlsanity.com/paper/2309.07145/) (2023)
- [Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach](https://mlsanity.com/paper/1812.07421/) (2018)
- [Masked Transformer for Electrocardiogram Classification](https://mlsanity.com/paper/2309.07136/) (2023)
- [MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring](https://mlsanity.com/paper/2301.12178/) (2023)
- [SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification](https://mlsanity.com/paper/2012.05510/) (2020)
- [IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification](https://mlsanity.com/paper/2204.05116/) (2022)
- [What comes after transformers? -- A selective survey connecting ideas in deep learning](https://mlsanity.com/paper/2408.00386/) (2024)

## Built on by (most cited first, 2 of 2)

- [BenchECG and xECG: a benchmark and baseline for ECG foundation models](https://mlsanity.com/paper/2509.10151/) (2025)
- [Failures Reveal What Metrics Miss: An Evidence-Driven Agent for Recursive Refinement of ECG Classifiers](https://mlsanity.com/paper/2607.24419/) (2026)

## Abstract (arXiv)

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for efficient and accurate diagnostic tools. Electrocardiograms (ECGs) are indispensable in diagnosing various heart conditions; however, their manual interpretation is time-consuming and error-prone. In this paper, we propose xLSTM-ECG, a novel approach that leverages an extended Long Short-Term Memory (xLSTM) network for multi-label classification of ECG signals, using the PTB-XL dataset. To the best of our knowledge, this work represents the first design and application of xLSTM modules specifically adapted for multi-label ECG classification. Our method employs a Short-Time Fourier Transform (STFT) to convert time-series ECG waveforms into the frequency domain, thereby enhancing feature extraction. The xLSTM architecture is specifically tailored to address the complexities of 12-lead ECG recordings by capturing both local and global signal features. Comprehensive experiments on the PTB-XL dataset reveal that our model achieves strong multi-label classification performance, while additional tests on the Georgia 12-Lead dataset underscore its robustness and efficiency. This approach significantly improves ECG classification accuracy, thereby advancing clinical diagnostics and patient care. The code will be publicly available upon acceptance.