ArticleBioengineering (Basel, Switzerland)2023
Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Heart Sound Classification for Early Detection of Cardiovascular Diseases Using XGBoost and Engineered Acoustic Features.Sensors (Basel, Switzerland) · 2026Article
- Article
- A Robust and Efficient Workflow for Heart Valve Disease Detection from PCG Signals: Integrating WCNN, MFCC Optimization, and Signal Quality Evaluation.Sensors (Basel, Switzerland) · 2025Article
- Attention-Fusion-Based Two-Stream Vision Transformer for Heart Sound Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Learning for Heart Sound Abnormality of Infants: Proof-of-Concept Study of 1D and 2D Representations.Children (Basel, Switzerland) · 2025Article
- Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals.Heliyon · 2024Article
- Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model.International journal of medical sciences · 2024Article
- Heart Sound Classification Network Based on Convolution and Transformer.Sensors (Basel, Switzerland) · 2023Article
- Densely Connected Networks with Multiple Features for Classifying Sound Signals with Reverberation.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The intelligent classification of heart-sound signals can assist clinicians in the rapid diagnosis of cardiovascular diseases. Mel-frequency cepstral coefficients (MelSpectrums) and log Mel-frequency cepstral coefficients (Log-MelSpectrums) based on a short-time Fourier transform (STFT) can represent the temporal and spectral structures of original heart-sound signals. Recently, various systems based on convolutional neural networks (CNNs) trained on the MelSpectrum and Log-MelSpectrum of segmental heart-sound frames that outperform systems using handcrafted features have been presented and classified heart-sound signals accurately. However, there is no a priori evidence of the best input representation for classifying heart sounds when using CNN models. Therefore, in this study, the MelSpectrum and Log-MelSpectrum features of heart-sound signals combined with a mathematical model of cardiac-sound acquisition were analysed theoretically. Both the experimental results and theoretical analysis demonstrated that the Log-MelSpectrum features can reduce the classification difference between domains and improve the performance of CNNs for heart-sound classification.
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Registered trials
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