ArticleMedical & biological engineering & computing2020
Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review on deep learning methods for heart sound signal analysis.Frontiers in artificial intelligence · 2024Pooled it
- Electronic Stethoscope Auscultation and Echocardiography in ARDS: Correlation and Prognostic Value for Mortality and ICU Length of Stay: A Prospective Observational Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Heart sound classification based on convolutional neural network with convolutional block attention module.Frontiers in physiology · 2025Article
- [Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications.Health data science · 2024Review
- Non-invasive ML methods for diagnosis of congenital heart disease associated with pulmonary arterial hypertension.Frontiers in physiology · 2024Review
- A Computer-Aided Heart Valve Disease Diagnosis System Based on Machine Learning.Journal of healthcare engineering · 2023Article
- A novel 1-D densely connected feature selection convolutional neural network for heart sounds classification.Annals of translational medicine · 2021Article
- An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm.Journal of healthcare engineering · 2021Article
- Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle signal was converted into a two-dimensional time-frequency picture using the MFSWT. Finally, two CNN models are trained using the aforementioned pictures. We combine two CNN models using sample entropy (SampEn) to determine which model is used to classify the heart sound signal. We evaluated our model on the heart sound public dataset provided by the PhysioNet Computing in Cardiology Challenge 2016. Experimental classification performance from a 10-fold cross-validation indicated that sensitivity (Se), specificity (Sp) and mean accuracy (MAcc) were 0.95, 0.93, and 0.94, respectively. The results showed the proposed method can classify normal and abnormal heart sounds with efficiency and high accuracy. Graphical abstract Block diagram of heart sound classification.
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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.