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dc.contributor.authorLei, Mengen_US
dc.contributor.authorLi, Jiaen_US
dc.contributor.authorLi, Mingen_US
dc.contributor.authorZou, Liangen_US
dc.contributor.authorYu, Hanen_US
dc.identifier.citationLei, M., Li, J., Li, M., Zou, L. & Yu, H. (2021). An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals. Diagnostics, 11(3), 534-.
dc.description.abstractCongestive 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.en_US
dc.rights© 2021 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleAn improved UNet++ model for congestive heart failure diagnosis using short-term RR intervalsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsCongestive Heart Failureen_US
dc.subject.keywordsShort-Term RR Intervalsen_US
dc.description.acknowledgementThis research was funded by the Fundamental Research Funds for the Central Universities with grant number 2019ZDPY17.en_US
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