ECG-QA: A Comprehensive Question Answering Dataset Combined With
Electrocardiogram
- URL: http://arxiv.org/abs/2306.15681v2
- Date: Sun, 24 Sep 2023 09:43:07 GMT
- Title: ECG-QA: A Comprehensive Question Answering Dataset Combined With
Electrocardiogram
- Authors: Jungwoo Oh, Gyubok Lee, Seongsu Bae, Joon-myoung Kwon, Edward Choi
- Abstract summary: ECG-QA is the first dataset specifically designed for ECG analysis.
The dataset comprises a total of 70 question templates that cover a wide range of clinically relevant ECG topics.
Our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs.
- Score: 12.167108953668464
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Question answering (QA) in the field of healthcare has received much
attention due to significant advancements in natural language processing.
However, existing healthcare QA datasets primarily focus on medical images,
clinical notes, or structured electronic health record tables. This leaves the
vast potential of combining electrocardiogram (ECG) data with these systems
largely untapped. To address this gap, we present ECG-QA, the first QA dataset
specifically designed for ECG analysis. The dataset comprises a total of 70
question templates that cover a wide range of clinically relevant ECG topics,
each validated by an ECG expert to ensure their clinical utility. As a result,
our dataset includes diverse ECG interpretation questions, including those that
require a comparative analysis of two different ECGs. In addition, we have
conducted numerous experiments to provide valuable insights for future research
directions. We believe that ECG-QA will serve as a valuable resource for the
development of intelligent QA systems capable of assisting clinicians in ECG
interpretations. Dataset URL: https://github.com/Jwoo5/ecg-qa
Related papers
- GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images [43.65650710265957]
We introduce GEM, the first MLLM unifying ECG time series, 12-lead ECG images and text for grounded and clinician-aligned ECG interpretation.
GEM enables feature-grounded analysis, evidence-driven reasoning, and a clinician-like diagnostic process through three core innovations.
We propose the Grounded ECG task, a clinically motivated benchmark designed to assess the MLLM's capability in grounded ECG understanding.
arXiv Detail & Related papers (2025-03-08T05:48:53Z) - ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis [8.059062779882554]
ECG-Expert-QA is a comprehensive dataset for evaluating diagnostic capabilities in electrocardiogram (ECG) interpretation.
It combines real-world clinical ECG data with systematically generated synthetic cases, covering 12 essential diagnostic tasks.
Key innovation is the support for multi-turn dialogues, enabling the development of conversational medical AI systems.
arXiv Detail & Related papers (2025-02-16T13:28:55Z) - An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains [17.809094003643523]
We introduce an ECG Foundation Model (ECGFounder) to broaden the diagnostic capabilities of ECG analysis.
ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database.
It achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses.
arXiv Detail & Related papers (2024-10-05T12:12:02Z) - Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling [19.513904491604794]
ECG-ReGen is a retrieval-based approach for ECG-to-text report generation and question answering.
By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen effectively analyzes ECG data and answers related queries.
arXiv Detail & Related papers (2024-09-13T12:50:36Z) - 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) - 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) - A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [52.31710895034573]
This work provides the first comprehensive review of healthcare knowledge graphs (HKGs)
It summarizes the pipeline and key techniques for HKG construction, as well as the common utilization approaches.
At the application level, we delve into the successful integration of HKGs across various health domains.
arXiv Detail & Related papers (2023-06-07T21:51:56Z) - Automated Cardiovascular Record Retrieval by Multimodal Learning between
Electrocardiogram and Clinical Report [28.608260758775316]
We introduce a novel approach to ECG interpretation, leveraging recent breakthroughs in Large Language Models (LLMs) and Vision-Transformer (ViT) models.
We propose an alternative method of automatically identifying the most similar clinical cases based on the input ECG data.
Our findings could serve as a crucial resource for providing diagnostic services in underdeveloped regions.
arXiv Detail & Related papers (2023-04-13T06:32:25Z) - 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) - 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) - Noise-Resilient Automatic Interpretation of Holter ECG Recordings [67.59562181136491]
We present a three-stage process for analysing Holter recordings with robustness to noisy signal.
First stage is a segmentation neural network (NN) with gradientdecoder architecture which detects positions of heartbeats.
Second stage is a classification NN which will classify heartbeats as wide or narrow.
Third stage is a boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features.
arXiv Detail & Related papers (2020-11-17T16:15:49Z) - 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.