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

• URL: http://arxiv.org/abs/2504.16101v1
• Date: Mon, 14 Apr 2025 16:12:46 GMT
• Title: xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM
• Authors: Lei Kang, Xuanshuo Fu, Javier Vazquez-Corral, Ernest Valveny, Dimosthenis Karatzas,
• Abstract summary: We propose xLSTM-ECG, a novel approach for multi-label classification of ECG signals.<n>To the best of our knowledge, this work represents the first design and application of xLSTM modules specifically adapted for multi-label ECG classification.
• Score: 14.02717596836022
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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