Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance

• URL: http://arxiv.org/abs/2410.13867v1
• Date: Wed, 02 Oct 2024 08:25:57 GMT
• Title: Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance
• Authors: Kuba Weimann, Tim O. F. Conrad,
• Abstract summary: We create a large unsupervised pre-training dataset by combining ten public ECG databases. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks.
• Score: 0.0
• License:
• Abstract: Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated datasets, which are difficult and costly to collect. To address this issue, transfer learning is often employed, where models are pre-trained on large datasets and fine-tuned for specific ECG classification tasks with limited labeled data. Self-supervised learning has become a widely adopted pre-training method, enabling models to learn meaningful representations from unlabeled datasets. In this work, we explore the joint-embedding predictive architecture (JEPA) for self-supervised learning from ECG data. Unlike invariance-based methods, JEPA does not rely on hand-crafted data augmentations, and unlike generative methods, it predicts latent features rather than reconstructing input data. We create a large unsupervised pre-training dataset by combining ten public ECG databases, amounting to over one million records. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks. Our results show that JEPA outperforms existing invariance-based and generative approaches, achieving an AUC of 0.945 on the PTB-XL all statements task. JEPA consistently learns the highest quality representations, as demonstrated in linear evaluations, and proves advantageous for pre-training even in the absence of additional data.

Related papers

• Self-Trained Model for ECG Complex Delineation [0.0]
  Electrocardiogram (ECG) delineation plays a crucial role in assisting cardiologists with accurate diagnoses. We introduce a dataset for ECG delineation and propose a novel self-trained method aimed at leveraging a vast amount of unlabeled ECG data. Our approach involves the pseudolabeling of unlabeled data using a neural network trained on our dataset. Subsequently, we train the model on the newly labeled samples to enhance the quality of delineation.
  arXiv  Detail & Related papers  (2024-06-04T18:54:10Z)
• Transfer Learning for Molecular Property Predictions from Small Data Sets [0.0]
  We benchmark common machine learning models for the prediction of molecular properties on two small data sets. We present a transfer learning strategy that uses large data sets to pre-train the respective models and allows to obtain more accurate models after fine-tuning on the original data sets.
  arXiv  Detail & Related papers  (2024-04-20T14:25:34Z)
• MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosis [1.3654846342364306]
  We introduce MELEP, a measure designed to estimate the effectiveness of knowledge transfer from a pre-trained model to a downstream ECG diagnosis task. Our experiments show that MELEP can predict the performance of pre-trained convolutional and recurrent deep neural networks, on small and imbalanced ECG data.
  arXiv  Detail & Related papers  (2023-10-27T14:57:10Z)
• Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis [4.3312979375047025]
  This paper proposes an unsupervised pre-training technique based on masked autoencoder (MAE) for electrocardiogram (ECG) signals. In addition, we propose a task-specific fine-tuning to form a complete framework for ECG analysis. The framework is high-level, universal, and not individually adapted to specific model architectures or tasks.
  arXiv  Detail & Related papers  (2023-10-17T11:19:51Z)
• Core-set Selection Using Metrics-based Explanations (CSUME) for multiclass ECG [2.0520503083305073]
  We show how a selection of good quality data improves deep learning model performance. Our experimental results show a 9.67% and 8.69% precision and recall improvement with a significant training data volume reduction of 50%.
  arXiv  Detail & Related papers  (2022-05-28T19:36:28Z)
• Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions [48.02011627390706]
  Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. In this paper, we apply unsupervised pre-training to heterogeneous, multi-modal EHR data for patient outcome prediction. We find that our proposed graph based pre-training method helps in modeling the data at a population level.
  arXiv  Detail & Related papers  (2022-03-23T17:59:45Z)
• 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)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)
• 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.