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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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[Heart sound denoising by dynamic noise estimation].

Chundong Xu1, Jing Zhou1, Dongwen Ying2

  • 1School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|November 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved heart sound denoising method to reduce noise distortion. The new technique enhances time-frequency features, improving accuracy in heart sound classification systems for better medical diagnosis.

Keywords:
heart sound classificationheart sound denoisingimproved minimum control recursive averagenoise estimateoptimally modified log-spectral amplitude

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Diagnostics

Background:

  • Traditional denoising methods like wavelet analysis and empirical mode decomposition often distort heart sounds.
  • Existing techniques struggle to effectively track and eliminate noise, impacting diagnostic accuracy.

Purpose of the Study:

  • To propose an advanced heart sound denoising method overcoming limitations of existing approaches.
  • To enhance the quality of heart sound signals for improved diagnostic applications.

Main Methods:

  • Developed a novel denoising technique using improved minimum control recursive average and optimally modified log-spectral amplitude.
  • Employed a short-time window for dynamic noise estimation and optimal spectrum gain function calculation.
  • Introduced a rigorous evaluation mechanism combining subjective spectral analysis and objective classification system performance.

Main Results:

  • The proposed method effectively improved time-frequency features of heart sound signals.
  • Achieved higher scores in normal and abnormal heart sound classification tasks.
  • Demonstrated significant noise reduction without causing substantial signal distortion.

Conclusions:

  • The novel denoising method offers superior performance compared to traditional techniques.
  • Enhances diagnostic accuracy for medical professionals and aids in developing computer-aided diagnosis systems.