xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDiagnosticECG ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSimilar Papers 제목 키워드 기반
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability
Predicting molecular properties is essential for drug discovery, and computational methods can greatly enhance this process. Molecular graphs have become a focus for representation learning, with Graph Neural Networks (G…
Drug DiscoveryMixture-of-ExpertsMolecular Property Predictionmolecular representation+3xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart
Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) have been pivotal in biomedical image segmentation, yet their ability to manage long-range dependencies remains constrained by inherent locality and comp…
3D Medical Imaging Segmentationimage-classificationImage ClassificationImage Segmentation+5XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder
Neurogliomas are among the most aggressive forms of cancer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behavior. Magnetic resonance imaging (MRI) is currentl…
Brain Tumor SegmentationDecoderMRI ReconstructionSegmentation+1CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory
In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current RS-CD methods lack a balanced consideration of performance and eff…
Change DetectionDomain Knowledge Integrated CNN-xLSTM-xAtt Network with Multi Stream Feature Fusion for Cuffless Blood Pressure Estimation from Photoplethysmography Signals
Estimating blood pressure (BP) from photoplethysmography (PPG) signals is challenging due to signal variability and noise, as well as the complex relationship between PPG and BP, which requires sophisticated algorithms a…
Blood pressure estimationPhotoplethysmography (PPG)