ETP: Learning Transferable ECG Representations via ECG-Text Pre-training

• URL: http://arxiv.org/abs/2309.07145v1
• Date: Wed, 6 Sep 2023 19:19:26 GMT
• Title: ETP: Learning Transferable ECG Representations via ECG-Text Pre-training
• Authors: Che Liu, Zhongwei Wan, Sibo Cheng, Mi Zhang, Rossella Arcucci
• Abstract summary: 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.
• Score: 10.856365645831728
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classification task in the ECG domain. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports. The proposed framework excels in both linear evaluation and zero-shot classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets, showcasing its ability for robust and generalizable cross-modal ECG feature learning.

Related papers

• Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model [25.131870247201636]
  We introduce a novel perspective on ECG signals, treating heartbeats as words and rhythms as sentences. We then propose HeartLang, a novel self-supervised learning framework for ECG language processing. We construct the largest heartbeat-based ECG vocabulary to date, which will further advance the development of ECG language processing.
  arXiv  Detail & Related papers  (2025-02-15T07:40:57Z)
• ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling [20.484166589932702]
  ECG-Byte is a tokenizer pipeline for autoregressive language modeling of ECGs. It compresses and encodes ECG signals into tokens, enabling end-to-end Large Language Models training. It achieves competitive performance in NLG tasks in only half the time and 48% of the data required by two-stage approaches.
  arXiv  Detail & Related papers  (2024-12-18T22:13:21Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals. Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality. The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture [0.0]
  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.
  arXiv  Detail & Related papers  (2024-10-11T06:30:48Z)
• ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text [14.06147507373525]
  This study introduces a new multimodal contrastive pretaining framework that aims to improve the quality and robustness of learned representations of 12-lead ECG signals. Our framework comprises two key components, including Cardio Query Assistant (CQA) and ECG Semantics Integrator(ESI)
  arXiv  Detail & Related papers  (2024-05-26T06:45:39Z)
• 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)
• Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder [69.7813498468116]
  We propose Contrastive EEG-Text Masked Autoencoder (CET-MAE), a novel model that orchestrates compound self-supervised learning across and within EEG and text. We also develop a framework called E2T-PTR (EEG-to-Text decoding using Pretrained Transferable Representations) to decode text from EEG sequences.
  arXiv  Detail & Related papers  (2024-02-27T11:45:21Z)
• 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)
• 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)
• Inductive Learning on Commonsense Knowledge Graph Completion [89.72388313527296]
  Commonsense knowledge graph (CKG) is a special type of knowledge graph (CKG) where entities are composed of free-form text. We propose to study the inductive learning setting for CKG completion where unseen entities may present at test time. InductivE significantly outperforms state-of-the-art baselines in both standard and inductive settings on ATOMIC and ConceptNet benchmarks.
  arXiv  Detail & Related papers  (2020-09-19T16:10:26Z)
• 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.