ArticleBMC medical informatics and decision making2023
Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Explainable electrocardiogram interpretation using deep learning-based semantic segmentation.European heart journal. Digital health · 2026Article
- An integrated algorithm for single lead electrocardiogram signal analysis using deep learning with 12-lead data.Scientific reports · 2025Article
- Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation.Frontiers in cardiovascular medicine · 2024Article
- Reference for Electrocardiographic Imaging-Based T-Wave Alternans Estimation.IEEE access : practical innovations, open solutions · 2024Article
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Authors and funding
8 authors.
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Abstract
backgroundSignal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manually assess the leads, as a variety of signal morphological variations in each lead have potential defects in recording, noise, or irregular heart rhythm/beat.
methodA computer-aided deep-learning algorithm is considered a state-of-the-art delineation model to classify ECG waveform and boundary in terms of the P-wave, QRS-complex, and T-wave and indicated the satisfactory result. This study implemented convolution layers as a part of convolutional neural networks for automated feature extraction and bidirectional long short-term memory as a classifier. For beat segmentation, we have experimented beat-based and patient-based approach.
resultsThe empirical results using both beat segmentation approaches, with a total of 14,588 beats were showed that our proposed model performed excellently well. All performance metrics above 95% and 93%, for beat-based and patient-based segmentation, respectively.
conclusionsThis is a significant step towards the clinical pertinency of automated 12-lead ECG delineation using deep learning.
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