Automatic Detection of Congestive Heart Failure Based on Multiscale Residual UNet++: From Centralized Learning to Federated Learning

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; pp. 1 - 13
• Main Authors: Zou, Liang, Huang, Zexin, Yu, Xinhui, Zheng, Jiannan, Liu, Aiping, Lei, Meng
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cardiovascular diseases; Centralized learning; Coders; congestive heart failure (CHF) detection; Decoders; Diagnosis; Electrocardiography; Feature extraction; Federated learning; federated learning (FL); Heart; Heart failure; Heart rate; Heart rate variability; Image segmentation; Medical imaging; multiscale residual module; Training; UNet
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3227955

Congestive heart failure (CHF) is a progressive and complex syndrome resulted from ventricular dysfunction, which is difficult to detect at early stages. Heart rate variability (HRV) has been identified as a prognostic indicator for CHF. The traditional diagnosis methods based on analyzing the electrocardiogram (ECG) are time-consuming and laborious, and the interpretation of the results is subjective. Inspired by the outstanding performance of U-shaped networks in medical image segmentation, in this article, we propose a novel end-to-end classification model based on 2000 intervals between successive R-peaks of ECG signals. The proposed model integrates the outputs of encoders, decoders, and intermediate units through a unified scale operation, which can not only preserve low-level details from the input signals but also extract the high-level pathology-related information. We further employ a variant of residual module with group convolution and squeeze-and-excitation (SE) block, enhancing the network's expression capability. In addition, considering the challenge of collecting large and diverse samples by individual institutions, we decentralize the data across different clients and extend the proposed model with a federated version, which is able to facilitate multi-institutional collaborations while maintaining data anonymity. A total of 29 CHF patients and 177 non-CHF subjects (i.e., 54 normal sinus rhythm (NSR) subjects, 84 atrial fibrillation (AF), and 39 Apnea subjects) from PhysioBank are included in this article. The experimental results show that the proposed model outperforms the state of the art both in centralized and decentralized learning, with an accuracy of 89.83% and 87.54%, respectively. The diagnosis model trained in federated framework provides competitive performance to that in centralized learning, which demonstrates its potential of utilizing multisite data to improve CHF detection performance without sharing patient privacy.