The heart sound classification of congenital heart disease by using median EEMD-Hurst and threshold denoising method
Xuankai Yang1, Jing Sun1, Hongbo Yang2
1School of Information Science and Engineering, Yunnan University, Kunming, 650504, China.
Insights
This study introduces a new method using median ensemble empirical mode decomposition (MEEMD) and neural networks to accurately classify heart sounds, improving congenital heart disease diagnosis by reducing noise interference.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Artificial Intelligence in Medicine
Background:
- Heart sound analysis is crucial for diagnosing congenital heart disease.
- Noise in heart sound acquisition significantly degrades diagnostic accuracy.
- Existing noise reduction methods risk removing weak pathological heart sound components.
Purpose of the Study:
- To develop a robust noise reduction and classification method for heart sound signals.
- To enhance the accuracy of machine-assisted diagnosis of congenital heart disease.
- To address limitations of existing denoising techniques that may filter out pathological signals.
Main Methods:
- Median Ensemble Empirical Mode Decomposition (MEEMD) for signal decomposition.
- Hurst analysis to identify noise-dominant Intrinsic Mode Functions (IMFs).
- Improved threshold denoising applied to identified IMFs, followed by signal reconstruction.
- Convolutional Neural Networks (CNNs) for classification using Mel spectral coefficients of denoised signals.
Main Results:
- MEEMD effectively suppressed mode mixing and splitting during signal decomposition.
- The novel denoising approach successfully preserved pathological components.
- Classification of normal and abnormal heart sounds achieved 93.8% accuracy, 93.1% specificity, and 94.6% sensitivity.
Conclusions:
- The proposed MEEMD-based denoising and CNN classification method offers a significant improvement for heart sound analysis.
- This approach enhances the reliability of machine-assisted diagnosis for congenital heart disease.
- The technique effectively reduces noise while preserving critical pathological information in heart sound signals.
Abstract:
Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that the pathological components of the signal are weak, which have the potential to be filtered out with the noise. In this research, a novel approach for classifying heart sounds based on median ensemble empirical mode decomposition (MEEMD), Hurst analysis, improved threshold denoising, and neural networks are presented. In decomposing the heart sound signal into several intrinsic mode functions (IMFs), mode mixing and mode splitting can be effectively suppressed by MEEMD. Hurst analysis is adopted for identifying the noisy content of IMFs. Then, the noise-dominated IMFs are denoised by an improved threshold function. Finally, the noise reduction signal is generated by reconstructing the processed components and the other components. A database of 5000 heart sounds from congenital heart disease and normal volunteers was constructed. The Mel spectral coefficients of the denoised signals were used as input vectors to the convolutional neural network for classification to verify the effectiveness of the preprocessing algorithm. An accuracy of 93.8%, a specificity of 93.1%, and a sensitivity of 94.6% were achieved for classifying the normal cases from abnormal one.
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