ArticleDiagnostics (Basel, Switzerland)2021
An Improved UNet++ Model for Congestive Heart Failure Diagnosis Using Short-Term RR Intervals.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- An ECG feature extraction method based on GASF and MPRE2D for the detection of congestive heart failure.Physical and engineering sciences in medicine · 2026Article
- [Advances in heart failure clinical research based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Decision Support Systems in HF based on Deep Learning Technologies.Current heart failure reports · 2022Review
- Image-to-image translation of label-free molecular vibrational images for a histopathological review using the UNet+/seg-cGAN model.Biomedical optics express · 2022Article
- An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy.Entropy (Basel, Switzerland) · 2021Article
- A Pulmonary Vascular Extraction Algorithm from Chest CT/CTA Images.Journal of healthcare engineering · 2021Article
- CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer.BioMed research international · 2021Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Congestive heart failure (CHF), a progressive and complex syndrome caused by ventricular dysfunction, is difficult to detect at an early stage. Heart rate variability (HRV) was proposed as a prognostic indicator for CHF. Inspired by the success of 2-D UNet++ in medical image segmentation, in this paper, we introduce an end-to-end encoder-decoder model to detect CHF using HRV signals. The developed model enhances the UNet++ model with Squeeze-and-Excitation (SE) residual blocks to extract deep features hierarchically and distinguish CHF patients from normal subjects. Two open-source databases are utilized for evaluating the proposed method, and three segment lengths of intervals between successive R-peaks are employed in comparison with state-of-the-art methods. The proposed method achieves an accuracy of 85.64%, 86.65% and 88.79% when 500, 1000 and 2000 RR intervals are utilized, respectively. It demonstrates that HRV evaluation based on deep learning can be an important tool for early detection of CHF, and may assist clinicians in achieving timely and accurate diagnoses.
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