Generalizing electrocardiogram delineation: training convolutional
neural networks with synthetic data augmentation
- URL: http://arxiv.org/abs/2111.12996v1
- Date: Thu, 25 Nov 2021 10:11:41 GMT
- Title: Generalizing electrocardiogram delineation: training convolutional
neural networks with synthetic data augmentation
- Authors: Guillermo Jimenez-Perez, Juan Acosta, Alejandro Alcaine, Oscar Camara
- Abstract summary: 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.
- Score: 63.51064808536065
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Obtaining per-beat information is a key task in the analysis of cardiac
electrocardiograms (ECG), as many downstream diagnosis tasks are dependent on
ECG-based measurements. Those measurements, however, are costly to produce,
especially in recordings that change throughout long periods of time. However,
existing annotated 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. The generation of conditions is controlled by imposing expert knowledge
on the generated trace, which increases the input variability for training the
model. 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. The best performing model obtained an $F_1$-score of
99.38\% and a delineation error of $2.19 \pm 17.73$ ms and $4.45 \pm 18.32$ ms
for all wave's fiducials (onsets and offsets, respectively), as averaged across
the P, QRS and T waves for three distinct freely available databases. The
excellent results were obtained despite the heterogeneous characteristics of
the tested databases, in terms of lead configurations (Holter, 12-lead),
sampling frequencies ($250$, $500$ and $2,000$ Hz) and represented
pathophysiologies (e.g., different types of arrhythmias, sinus rhythm with
structural heart disease), hinting at its generalization capabilities, while
outperforming current state-of-the-art delineation approaches.
Related papers
- FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data [52.55123685248105]
Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment.
Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality.
This paper presents the first real-world FL benchmark for cardiovascular disease detection, named FedCVD.
arXiv Detail & Related papers (2024-10-28T02:24:01Z) - 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) - Understanding of Normal and Abnormal Hearts by Phase Space Analysis and
Convolutional Neural Networks [0.0]
His-Purkinje network is used to analyze a normal human heart's power spectra.
CNNs method is applied to 44 records via the MIT-BIH database recorded with MLII.
Binary CNN classification is used to determine healthy or unhealthy hearts.
arXiv Detail & Related papers (2023-05-16T19:52:40Z) - Arrhythmia Classification using CGAN-augmented ECG Signals [8.819736346681463]
Generative Adrial Networks (GAN) are used to generate realistic synthetic ECG signals.
This study investigates the impact of data augmentation on arrhythmia classification.
arXiv Detail & Related papers (2022-01-26T17:41:57Z) - Synthetic ECG Signal Generation Using Generative Neural Networks [7.122393663641668]
We studied the synthetic ECG generation capability of 5 different models from the generative adversarial network (GAN) family.
The results show that all the tested models can to an extent successfully mass-generate acceptable heartbeats with high similarity in morphological features.
arXiv Detail & Related papers (2021-12-05T20:28:55Z) - SE-ECGNet: A Multi-scale Deep Residual Network with
Squeeze-and-Excitation Module for ECG Signal Classification [6.124438924401066]
We develop a multi-scale deep residual network for the ECG signal classification task.
We are the first to propose to treat the multi-lead signal as a 2-dimensional matrix.
Our proposed model achieves 99.2% F1-score in the MIT-BIH dataset and 89.4% F1-score in Alibaba dataset.
arXiv Detail & Related papers (2020-12-10T08:37:44Z) - An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem.
We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
arXiv Detail & Related papers (2020-12-06T18:55:07Z) - Multilabel 12-Lead Electrocardiogram Classification Using Gradient
Boosting Tree Ensemble [64.29529357862955]
We build an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis.
For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform.
We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class.
arXiv Detail & Related papers (2020-10-21T18:11:36Z) - 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.