Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture

• URL: http://arxiv.org/abs/2410.08559v4
• Date: Tue, 03 Dec 2024 03:21:51 GMT
• Title: Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture
• Authors: Sehun Kim,
• Abstract summary: We introduce ECG-JEPA, a self-supervised learning model for 12-lead ECG analysis. It learns semantic representations of ECG data by predicting in the hidden latent space. ECG-JEPA achieves state-of-the-art performance in various downstream tasks including ECG classification and feature prediction.
• Score: 0.0
• License:
• Abstract: Electrocardiogram (ECG) captures the heart's electrical signals, offering valuable information for diagnosing cardiac conditions. However, the scarcity of labeled data makes it challenging to fully leverage supervised learning in medical domain. Self-supervised learning (SSL) offers a promising solution, enabling models to learn from unlabeled data and uncover meaningful patterns. In this paper, we show that masked modeling in the latent space can be a powerful alternative to existing self-supervised methods in the ECG domain. We introduce ECG-JEPA, a SSL model for 12-lead ECG analysis that learns semantic representations of ECG data by predicting in the hidden latent space, bypassing the need to reconstruct raw signals. This approach offers several advantages in the ECG domain: (1) it avoids producing unnecessary details, such as noise, which is common in ECG; and (2) it addresses the limitations of na\"ive L2 loss between raw signals. Another key contribution is the introduction of Cross-Pattern Attention (CroPA), a specialized masked attention mechanism tailored for 12-lead ECG data. ECG-JEPA is trained on the union of several open ECG datasets, totaling approximately 180,000 samples, and achieves state-of-the-art performance in various downstream tasks including ECG classification and feature prediction. Our code is openly available at https://github.com/sehunfromdaegu/ECG_JEPA.

Related papers

• Deep learning model for ECG reconstruction reveals the information content of ECG leads [0.0]
  This study introduces a deep learning model based on the U-net architecture to reconstruct missing leads in electrocardiograms (ECGs) Using publicly available datasets, the model was trained to regenerate 12-lead ECG data. The results highlight the ability of the model to quantify the information content of each ECG lead and their inter-lead correlations.
  arXiv  Detail & Related papers  (2025-02-01T21:06:07Z)
• AnyECG: Foundational Models for Electrocardiogram Analysis [36.53693619144332]
  Electrocardiogram (ECG) is highly sensitive in detecting acute heart attacks. This paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data. Experimental results in anomaly detection, arrhythmia detection, corrupted lead generation, and ultra-long ECG signal analysis demonstrate that AnyECG learns common ECG knowledge from data and significantly outperforms cutting-edge methods in each respective task.
  arXiv  Detail & Related papers  (2024-11-17T17:32:58Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• ECG-FM: An Open Electrocardiogram Foundation Model [3.611746032873298]
  We present ECG-FM, an open foundation model for ECG analysis. ECG-FM adopts a transformer-based architecture and is pretrained on 2.5 million samples. We show how its command of contextual information results in strong performance, rich pretrained embeddings, and reliable interpretability.
  arXiv  Detail & Related papers  (2024-08-09T17:06:49Z)
• MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [41.324530807795256]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation. We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)
• ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal [19.885905393439014]
  We propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals. Based on the structural features, a temporal model is designed to learn the temporal information for various clinical tasks. The proposed method outperforms the baseline model and shows competitive performances compared with task-specific methods in three clinical applications.
  arXiv  Detail & Related papers  (2023-10-01T23:17:55Z)
• ETP: Learning Transferable ECG Representations via ECG-Text Pre-training [10.856365645831728]
  ECG-Text Pre-training (ETP) is an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports.
  arXiv  Detail & Related papers  (2023-09-06T19:19:26Z)
• PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines [46.09869227806991]
  evaluating canine electrocardiograms (ECG) require skilled veterinarians. Current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal.
  arXiv  Detail & Related papers  (2023-05-17T09:06:39Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.