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 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.

Automated summary

This study introduces a deep learning model based on HRV signals for early detection of congestive heart failure (CHF), with accuracy of 85.64%, 86.65% and 88.79% when utilizing 500, 1000 and 2000 RR intervals. The model enhances the UNet++ model with Squeeze-and-Excitation (SE) residual blocks to effectively distinguish CHF patients from normal subjects.