xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM
Journal:
arXiv
Published Date:
Apr 14, 2025
Abstract
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.
