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Enhancing cross-domain robustness in phonocardiogram signal classification using domain-invariant preprocessing and

Arnab Maity1, Goutam Saha1

  • 1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.

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Summary
This summary is machine-generated.

This study enhances cardiovascular disease detection using phonocardiogram (PCG) signals by developing robust machine learning methods. The approach improves accuracy across different datasets, making automated cardiac screening more reliable.

Keywords:
Cross-domain evaluationData imbalancePhonocardiogramPreprocessingTransfer learningWavelet transform

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Area of Science:

  • Biomedical Signal Processing
  • Cardiovascular Diagnostics
  • Machine Learning in Healthcare

Background:

  • Phonocardiogram (PCG) signal analysis offers a non-invasive method for diagnosing cardiovascular diseases.
  • Current machine learning (ML) approaches for PCG analysis struggle with performance variations across different datasets due to varying data acquisition settings.
  • This variability significantly impacts the reliability of automated disease detection systems.

Purpose of the Study:

  • To investigate the impact of data acquisition parameter variations on PCG data from different databases.
  • To develop robust methods for PCG-based cardiovascular disease detection that are resilient to cross-dataset variations.
  • To enhance the real-world applicability of automated cardiac screening systems.

Main Methods:

  • Employed a combination of domain-invariant preprocessing, transfer learning, and domain-balanced variable hop fragment selection (DBVHFS).
  • Domain-invariant preprocessing normalized PCG signals to minimize stethoscope and environmental variations.
  • Transfer learning utilized pre-trained audio models for generalized feature representation, and DBVHFS ensured balanced training fragment distribution across all domains.

Main Results:

  • The proposed method was evaluated on six independent PhysioNet/CinC Challenge 2016 PCG databases using a leave-one-dataset-out cross-validation strategy.
  • Achieved a relative improvement of 5.92% in unweighted average recall and 17.71% in sensitivity compared to existing methods.
  • Demonstrated superior performance in cross-dataset evaluations, highlighting the system's robustness.

Conclusions:

  • The developed methods effectively address variations in PCG data from diverse sources.
  • The proposed approach shows significant potential for improving the reliability and implementation of automated cardiac screening systems in clinical practice.
  • Enhanced robustness against data variability paves the way for more widespread adoption of AI in cardiovascular diagnostics.