Deep learning based ECG segmentation for delineation of diverse
arrhythmias
- URL: http://arxiv.org/abs/2304.06237v2
- Date: Wed, 6 Sep 2023 10:13:09 GMT
- Title: Deep learning based ECG segmentation for delineation of diverse
arrhythmias
- Authors: Chankyu Joung, Mijin Kim, Taejin Paik, Seong-Ho Kong, Seung-Young Oh,
Won Kyeong Jeon, Jae-hu Jeon, Joong-Sik Hong, Wan-Joong Kim, Woong Kook,
Myung-Jin Cha, Otto van Koert
- Abstract summary: This study builds on existing research by introducing a U-Net-like segmentation model for ECG delineation.
Key contributions include identifying segmentation model failures in different arrhythmia types, developing a robust model using a diverse training set, and introducing a classification guided strategy to reduce false P wave predictions for specific arrhythmias.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate delineation of key waveforms in an ECG is a critical initial step in
extracting relevant features to support the diagnosis and treatment of heart
conditions. Although deep learning based methods using a segmentation model to
locate the P, QRS, and T waves have shown promising results, their ability to
handle signals exhibiting arrhythmia remains unclear. This study builds on
existing research by introducing a U-Net-like segmentation model for ECG
delineation, with a particular focus on diverse arrhythmias. For this purpose,
we curate an internal dataset containing waveform boundary annotations for
various arrhythmia types to train and validate our model. Our key contributions
include identifying segmentation model failures in different arrhythmia types,
developing a robust model using a diverse training set, achieving comparable
performance on benchmark datasets, and introducing a classification guided
strategy to reduce false P wave predictions for specific arrhythmias. This
study advances deep learning based ECG delineation in the context of
arrhythmias and highlights its challenges.
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) - Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals [4.056982620027252]
We develop a vision transformer approach to identify atrial fibrillation based on single-lead ECG data.
A residual network (ResNet) approach is also developed for comparison with the vision transformer approach.
arXiv Detail & Related papers (2024-02-12T11:04:08Z) - Improving Diffusion Models for ECG Imputation with an Augmented Template
Prior [43.6099225257178]
noisy and poor-quality recordings are a major issue for signals collected using mobile health systems.
Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models.
We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
arXiv Detail & Related papers (2023-10-24T11:34:15Z) - 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) - DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial
Attention Detection [49.196182908826565]
Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment.
Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images.
This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
arXiv Detail & Related papers (2023-09-07T13:43:46Z) - Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites:
A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area.
We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions.
We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
arXiv Detail & Related papers (2023-08-31T00:36:10Z) - MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining
Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS)
MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020.
The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
arXiv Detail & Related papers (2022-01-10T06:37:23Z) - Machine Learning-based Efficient Ventricular Tachycardia Detection Model
of ECG Signal [0.0]
In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role.
This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a machine learning-based classifier model.
arXiv Detail & Related papers (2021-12-24T05:56:09Z) - 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) - 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) - Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia
Classification [0.0]
Cardiac arrhythmia is a prevalent and significant cause of mortality and morbidity among cardiac ailments.
Deep Learning methods have provided solutions to performing arrhythmia diagnosis at scale.
There is a dire need to correlate the obtained model outputs to the corresponding segments of the ECG.
The first method is a novel application of Gradient-weighted Class Activation Map (Grad-CAM) for visualizing the saliency of the CNN model.
In the second approach, saliency is derived by learning the input deletion mask for the LSTM model.
arXiv Detail & Related papers (2020-04-11T13:24:17Z)
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.