ArticleBiomedical engineering online2020
Gated recurrent unit-based heart sound analysis for heart failure screening.
Article in Biomedical engineering online, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 51 citations in OpenAlex.
- Preliminary results of a single, easy-to-use device for multimodal detection of intervals associated with heart failure decompensation.European heart journal. Digital health · 2026Article
- Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.Journal of imaging informatics in medicine · 2026Article
- Streamlining heart failure patient care with machine learning of thoracic cavity sound data.World journal of cardiology · 2025Review
- Integration of PCG spectrogram texture and deep features for the diagnosis of heart failure with preserved ejection fraction using heterogeneous stacking ensemble learning.Frontiers in physiology · 2025Article
- [Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Optimizing extubation success: a comparative analysis of time series algorithms and activation functions.Frontiers in computational neuroscience · 2024Article
- Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications.Health data science · 2024Review
- Applications of deep learning models in precision prediction of survival rates for heart failure patients.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Association between phonocardiography and echocardiography in heart failure patients with preserved ejection fraction.European heart journal. Digital health · 2023Article
- Reviving the origins: acoustic biomarkers of heart failure with preserved ejection fraction.European heart journal. Digital health · 2023Article
- [Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Decision Support Systems in HF based on Deep Learning Technologies.Current heart failure reports · 2022Review
- Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods.Frontiers in cardiovascular medicine · 2022Review
- Identification of decompensation episodes in chronic heart failure patients based solely on heart sounds.Frontiers in cardiovascular medicine · 2022Article
- Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma.Emergency medicine international · 2022Article
- Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis.Diagnostics (Basel, Switzerland) · 2021Article
- Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network.Biomedical engineering online · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundHeart 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.
methodsWe 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.
resultsTo 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.
conclusionThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.