ArticleFrontiers in cardiovascular medicine2024
Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Precision medicine and personalized nursing in cardiovascular disease: clinical applications and frontier developments.Frontiers in cardiovascular medicine · 2026Review
- Age estimation via electrocardiogram from smartwatches.npj biomedical innovations · 2025Article
- Advanced electrocardiography heart age: a prognostic, explainable machine learning approach applicable to sinus and non-sinus rhythms.European heart journal. Digital health · 2025Article
- Heart Rate Variability in Relation to Cardiovascular Autonomic Neuropathy Among Patients at an Urban Hospital in Kazakhstan.International journal of environmental research and public health · 2024Article
- GeoCrack: A High-Resolution Dataset For Segmentation of Fracture Edges in Geological Outcrops.Scientific data · 2024Article
- The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results.BMC medical imaging · 2024Article
- Spatial attention-based CSR-Unet framework for subdural and epidural hemorrhage segmentation and classification using CT images.BMC medical imaging · 2024Article
- Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings.BMC medical imaging · 2024Article
- The value evaluation of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection.BMC medical imaging · 2024Article
- Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.BMC medical imaging · 2024Article
- An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images.BMC medical imaging · 2024Article
- Metal implant segmentation in CT images based on diffusion model.BMC medical imaging · 2024Article
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5 authors.
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Abstract
Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity produced by the contraction and relaxation of the cardiac muscles. It has been established in the literature that the difference between ECG-derived age and chronological age represents a general measure of cardiovascular health. Elevated ECG-derived age strongly correlates with cardiovascular conditions (e.g., atherosclerotic cardiovascular disease). However, the neural networks for ECG age estimation are yet to be thoroughly evaluated from the perspective of ECG acquisition parameters. Additionally, deep learning systems for ECG analysis encounter challenges in generalizing across diverse ECG morphologies in various ethnic groups and are susceptible to errors with signals that exhibit random or systematic distortions To address these challenges, we perform a comprehensive empirical study to determine the threshold for the sampling rate and duration of ECG signals while considering their impact on the computational cost of the neural networks. To tackle the concern of ECG waveform variability in different populations, we evaluate the feasibility of utilizing pre-trained and fine-tuned networks to estimate ECG age in different ethnic groups. Additionally, we empirically demonstrate that finetuning is an environmentally sustainable way to train neural networks, and it significantly decreases the ECG instances required (by more than
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