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Vibration pattern recognition using a compressed histogram of oriented gradients for snoring source analysis.

Yi Zhang1, Zhao Zhao1, Hui-Jie Xu2

  • 1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.

Bio-Medical Materials and Engineering
|June 1, 2020
PubMed
Summary
This summary is machine-generated.

A new acoustic feature, compressed histogram of oriented gradients (CHOG), accurately identifies snoring vibration patterns. This method aids surgical decisions for simple snorers and obstructive sleep apnea/hypopnea syndrome (OSAHS) patients.

Keywords:
Snoring source analysiscompressed histogram of oriented gradientsmultilinear principal component analysisvibration pattern recognition

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Area of Science:

  • Biomedical Engineering
  • Acoustic Analysis
  • Sleep Medicine

Background:

  • Snoring source analysis is crucial for surgical planning in simple snorers and obstructive sleep apnea/hypopnea syndrome (OSAHS) patients.
  • Snoring sounds contain vital information about upper airway tissue vibrations.

Purpose of the Study:

  • To introduce a novel acoustic feature, compressed histogram of oriented gradients (CHOG), for recognizing snoring vibration patterns.
  • To evaluate the efficacy of CHOG in classifying vibration patterns associated with specific upper airway tissues.

Main Methods:

  • Developed CHOG by compressing histogram of oriented gradients (HOG) descriptors using multilinear principal component analysis (MPCA).
  • Analyzed 1037 snoring events from 76 patients undergoing drug-induced sleep endoscopy (DISE).
  • Utilized a support vector machine (SVM) classifier to recognize vibration patterns.

Main Results:

  • The CHOG feature achieved an 89.8% recognition accuracy for seven distinct vibration patterns.
  • Successfully categorized vibration patterns corresponding to soft palate, lateral pharyngeal wall, tongue base, and epiglottis.
  • Demonstrated superior performance compared to existing single acoustic features.

Conclusions:

  • CHOG is a highly accurate feature for acoustic analysis of snoring vibration sources.
  • This method offers a promising tool for non-invasive snoring diagnosis and surgical planning.
  • The findings support the use of CHOG in differentiating vibration patterns for improved patient management.