Scholarlink

<p>Phonocardiogram (PCG) analysis is an inexpensive and non-invasive technique for the automatic diagnosis of heart valve diseases. In the clinical domain, PCG recordings are typically corrupted by noise, inter-subject variability, and overlapping signal characteristics, necessitating uncertainty-based decision support. To solve this problem, this paper presents an uncertainty-aware deep multimodal early fusion network (DMEFNet) that jointly integrates one-dimensional(1D) temporal signals and two-dimensional (2D) time–frequency image representations for multiclass PCG classification. Four main uncertainty quantification (UQ) methods, namely Monte Carlo (MC) dropout, Bayesian Neural Networks (BNNs), Deep Ensembles (DE), and Dirichlet-based Evidential Deep Learning (EDL), are used for predictive uncertainty estimation. Extensive experimental evaluations on the public HVD dataset demonstrated that uncertainty estimates are well-calibrated, scoring low predictive uncertainty when samples are correctly classified and higher uncertainty for ambiguous or noise samples. To the best of our knowledge, this work is among the first few attempts to analyze UQ in PCG-based heart valve disease classification. The introduced framework increases clinical trust, reliability and enables risk-aware decisions, thereby promoting the development of PCG-based diagnostic systems for real-world clinical applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Uncertainty quantification-based DMEFNet for reliable modelling of heart sound signals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • K. P. Suchithra,
  • Neethu Mohan,
  • U. Rajendra Acharya,
  • S. Sachin Kumar

摘要

Phonocardiogram (PCG) analysis is an inexpensive and non-invasive technique for the automatic diagnosis of heart valve diseases. In the clinical domain, PCG recordings are typically corrupted by noise, inter-subject variability, and overlapping signal characteristics, necessitating uncertainty-based decision support. To solve this problem, this paper presents an uncertainty-aware deep multimodal early fusion network (DMEFNet) that jointly integrates one-dimensional(1D) temporal signals and two-dimensional (2D) time–frequency image representations for multiclass PCG classification. Four main uncertainty quantification (UQ) methods, namely Monte Carlo (MC) dropout, Bayesian Neural Networks (BNNs), Deep Ensembles (DE), and Dirichlet-based Evidential Deep Learning (EDL), are used for predictive uncertainty estimation. Extensive experimental evaluations on the public HVD dataset demonstrated that uncertainty estimates are well-calibrated, scoring low predictive uncertainty when samples are correctly classified and higher uncertainty for ambiguous or noise samples. To the best of our knowledge, this work is among the first few attempts to analyze UQ in PCG-based heart valve disease classification. The introduced framework increases clinical trust, reliability and enables risk-aware decisions, thereby promoting the development of PCG-based diagnostic systems for real-world clinical applications.