Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection

• URL: http://arxiv.org/abs/2306.01249v1
• Date: Fri, 2 Jun 2023 03:23:16 GMT
• Title: Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection
• Authors: Zibin Zhao
• Abstract summary: We present an in-depth review of transformer architectures that are applied to ECG classification. These models capture complex temporal relationships in ECG signals that other models might overlook. This review serves as a valuable resource for researchers and practitioners and aims to shed light on this innovative application in ECG interpretation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The emergence of deep learning has significantly enhanced the analysis of electrocardiograms (ECGs), a non-invasive method that is essential for assessing heart health. Despite the complexity of ECG interpretation, advanced deep learning models outperform traditional methods. However, the increasing complexity of ECG data and the need for real-time and accurate diagnosis necessitate exploring more robust architectures, such as transformers. Here, we present an in-depth review of transformer architectures that are applied to ECG classification. Originally developed for natural language processing, these models capture complex temporal relationships in ECG signals that other models might overlook. We conducted an extensive search of the latest transformer-based models and summarize them to discuss the advances and challenges in their application and suggest potential future improvements. This review serves as a valuable resource for researchers and practitioners and aims to shed light on this innovative application in ECG interpretation.

Related papers

• ECG Arrhythmia Detection Using Disease-specific Attention-based Deep Learning Model [0.0]
  We propose a disease-specific attention-based deep learning model (DANet) for arrhythmia detection from short ECG recordings. The novel idea is to introduce a soft-coding or hard-coding waveform enhanced module into existing deep neural networks. For the soft-coding DANet, we also develop a learning framework combining self-supervised pre-training with two-stage supervised training.
  arXiv  Detail & Related papers  (2024-07-25T13:27:10Z)
• Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey [6.1689808850463335]
  Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. Recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference. This paper aims to provide a comprehensive review of the methods of solving ECG inverse problem, the validation strategies, the clinical applications, and future perspectives.
  arXiv  Detail & Related papers  (2024-06-17T11:57:14Z)
• Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction [42.95604565673447]
  This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch generative models. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/ computed tomography (CT) datasets.
  arXiv  Detail & Related papers  (2024-04-12T18:21:08Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• 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)
• HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes [28.88454028731653]
  We create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We show that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. We also show that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs.
  arXiv  Detail & Related papers  (2022-12-13T16:39:21Z)
• A Transformer Architecture for Stress Detection from ECG [7.559720049837459]
  We present a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. Our experiments show that the proposed model achieves strong results, comparable or better than the state-of-the-art models for ECG-based stress detection.
  arXiv  Detail & Related papers  (2021-08-22T14:34:44Z)
• Physics-constrained Deep Learning for Robust Inverse ECG Modeling [0.913755431537592]
  This paper presents a physics-constrained deep learning (P-DL) framework for high-dimensional inverse ECG modeling. The proposed P-DL approach is implemented to solve the inverse ECG model and predict the time-varying distribution of electric potentials in the heart.
  arXiv  Detail & Related papers  (2021-07-26T01:30:41Z)
• SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification [37.73516738836885]
  We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. We use a system of ordinary differential equations representing heart dynamics to create biologically plausible ECG training examples.
  arXiv  Detail & Related papers  (2020-06-27T12:17:21Z)
• 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)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

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.