ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases

• URL: http://arxiv.org/abs/2207.10670v2
• Date: Mon, 29 May 2023 15:22:35 GMT
• Title: ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
• Authors: Jintai Chen, Kuanlun Liao, Kun Wei, Haochao Ying, Danny Z. Chen, Jian Wu
• Abstract summary: We propose a disease-aware generative adversarial network for multi-view ECG synthesis called ME-GAN. Since ECG manifestations of heart diseases are often localized in specific waveforms, we propose a new "mixup normalization" to inject disease information precisely into suitable locations. Comprehensive experiments verify that our ME-GAN performs well on multi-view ECG signal synthesis with trusty morbid manifestations.
• Score: 24.52989747071257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases. Many studies have devised ECG analysis models (e.g., classifiers) to assist diagnosis. As an upstream task, researches have built generative models to synthesize ECG data, which are beneficial to providing training samples, privacy protection, and annotation reduction. However, previous generative methods for ECG often neither synthesized multi-view data, nor dealt with heart disease conditions. In this paper, we propose a novel disease-aware generative adversarial network for multi-view ECG synthesis called ME-GAN, which attains panoptic electrocardio representations conditioned on heart diseases and projects the representations onto multiple standard views to yield ECG signals. Since ECG manifestations of heart diseases are often localized in specific waveforms, we propose a new "mixup normalization" to inject disease information precisely into suitable locations. In addition, we propose a view discriminator to revert disordered ECG views into a pre-determined order, supervising the generator to obtain ECG representing correct view characteristics. Besides, a new metric, rFID, is presented to assess the quality of the synthesized ECG signals. Comprehensive experiments verify that our ME-GAN performs well on multi-view ECG signal synthesis with trusty morbid manifestations.

Related papers

• DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information [13.680337221159506]
  Heart disease remains a significant threat to human health. Scarcity of high-quality ECG data, driven by privacy concerns and limited medical resources, creates a pressing need for effective ECG signal generation. We propose DiffuSETS, a novel framework capable of generating ECG signals with high semantic alignment and fidelity.
  arXiv  Detail & Related papers  (2025-01-10T12:55:34Z)
• 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)
• 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)
• 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)
• Leveraging Statistical Shape Priors in GAN-based ECG Synthesis [3.3482093430607267]
  We propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals. Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines.
  arXiv  Detail & Related papers  (2022-10-22T18:06:11Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)

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.