An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals
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 pa...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/153896 |
| _version_ | 1811696196582899712 |
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| author | Lei, Meng Li, Jia Li, Ming Zou, Liang Yu, Han |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Lei, Meng Li, Jia Li, Ming Zou, Liang Yu, Han |
| author_sort | Lei, Meng |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:35:31Z |
| format | Journal Article |
| id | ntu-10356/153896 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:35:31Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1538962021-12-30T08:37:32Z An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals Lei, Meng Li, Jia Li, Ming Zou, Liang Yu, Han School of Computer Science and Engineering Engineering::Computer science and engineering Congestive Heart Failure Short-Term RR Intervals 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. Published version This research was funded by the Fundamental Research Funds for the Central Universities with grant number 2019ZDPY17. 2021-12-30T08:37:32Z 2021-12-30T08:37:32Z 2021 Journal Article Lei, 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-. https://dx.doi.org/10.3390/diagnostics11030534 2075-4418 https://hdl.handle.net/10356/153896 10.3390/diagnostics11030534 33809773 2-s2.0-85108988628 3 11 534 en Diagnostics © 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:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Engineering::Computer science and engineering Congestive Heart Failure Short-Term RR Intervals Lei, Meng Li, Jia Li, Ming Zou, Liang Yu, Han An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals |
| title | An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals |
| title_full | An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals |
| title_fullStr | An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals |
| title_full_unstemmed | An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals |
| title_short | An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals |
| title_sort | improved unet model for congestive heart failure diagnosis using short term rr intervals |
| topic | Engineering::Computer science and engineering Congestive Heart Failure Short-Term RR Intervals |
| url | https://hdl.handle.net/10356/153896 |
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