Uncertainty quantification-based DMEFNet for reliable modelling of heart sound signals
K P Suchithra1, Neethu Mohan2, U Rajendra Acharya3
1Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Scientific Reports
|May 26, 2026
Summary
This study introduces an uncertainty-aware deep multimodal early fusion network (DMEFNet) for diagnosing heart valve diseases using phonocardiogram (PCG) analysis. The network effectively quantifies uncertainty, improving diagnostic reliability for clinical applications.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Phonocardiogram (PCG) analysis offers a cost-effective, non-invasive method for diagnosing heart valve diseases.
- Clinical PCG data often suffers from noise, variability, and overlapping signals, requiring robust decision support systems.
- Accurate diagnosis is crucial for effective patient management and treatment.
Purpose of the Study:
- To develop an uncertainty-aware deep multimodal early fusion network (DMEFNet) for enhanced PCG classification.
- To integrate 1D temporal signals and 2D time-frequency representations for improved diagnostic accuracy.
- To evaluate the effectiveness of various uncertainty quantification (UQ) methods in PCG analysis.
Main Methods:
- Implementation of a deep multimodal early fusion network (DMEFNet) integrating 1D PCG signals and 2D time-frequency images.
- Application of four UQ methods: Monte Carlo (MC) dropout, Bayesian Neural Networks (BNNs), Deep Ensembles (DE), and Dirichlet-based Evidential Deep Learning (EDL).
- Extensive experimental validation using the public HVD dataset for multiclass PCG classification.
Main Results:
- The DMEFNet demonstrated well-calibrated uncertainty estimates across different classification scenarios.
- Low predictive uncertainty was observed for correctly classified samples.
- Higher uncertainty was associated with ambiguous or noisy PCG samples, indicating robust performance.
Conclusions:
- The developed DMEFNet effectively integrates multimodal PCG data and UQ for reliable heart valve disease classification.
- This approach enhances clinical trust and enables risk-aware decision-making in diagnostic systems.
- The findings support the advancement of PCG-based diagnostics for real-world clinical implementation.
Keywords:
Deep multimodal fusionHeart valve diseasesPhonocardiogram signalUncertainty estimationUncertainty quantificationclassificationMore Related Videos
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