Gated recurrent unit-based heart sound analysis for heart failure screening

• Published in: Biomedical engineering online Vol. 19; no. 1; pp. 3 - 17
• Main Authors: Gao, Shan, Zheng, Yineng, Guo, Xingming
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.01.2020; BioMed Central Ltd; BMC
• Subjects: Accuracy; Artificial neural networks; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; Cardiovascular diseases; Congestive heart failure; Coronary artery disease; Deep learning; Diagnosis; Disease; Engineering; Feature extraction; Gated recurrent unit; Health aspects; Heart failure; Heart failure screening; Heart sound; Heart sounds; Markov chains; Methods; Mortality; Neural networks; Performance evaluation; Physiology; Regression analysis; Regression models; Screening; Signal processing; Sound; Structure-function relationships; Support vector machines; Wavelet transforms; China; Deep learning; Gated recurrent unit; Heart sound; Heart failure screening
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-020-0747-x

Background Heart failure (HF) is a type of cardiovascular disease caused by abnormal cardiac structure and function. Early screening of HF has important implication for treatment in a timely manner. Heart sound (HS) conveys relevant information related to HF; this study is therefore based on the analysis of HS signals. The objective is to develop an efficient tool to identify subjects of normal, HF with preserved ejection fraction and HF with reduced ejection fraction automatically. Methods We proposed a novel HF screening framework based on gated recurrent unit (GRU) model in this study. The logistic regression-based hidden semi-Markov model was adopted to segment HS frames. Normalized frames were taken as the input of the proposed model which can automatically learn the deep features and complete the HF screening without de-nosing and hand-crafted feature extraction. Results To evaluate the performance of proposed model, three methods are used for comparison. The results show that the GRU model gives a satisfactory performance with average accuracy of 98.82%, which is better than other comparison models. Conclusion The proposed GRU model can learn features from HS directly, which means it can be independent of expert knowledge. In addition, the good performance demonstrates the effectiveness of HS analysis for HF early screening.