Automatic snoring detection from nasal pressure data

Hyo Ki Lee, Jeon Lee, Hojoong Kim, Kyoung Joung Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This study presents a method for automatic snoring detection from a nasal pressure data. First, a spectrogram analysis was performed in order to obtain information about the spectral characteristic of nasal pressure data. The automatic method is based on a simple signal filtering and short-time energy technique. Fifteen patients were participated in order to evaluation the performance of the proposed method. Results are compared with manually labeled snoring events by watching video records. The sensitivity and positive predictivity value were 93.73% and 93.70%, respectively. The results in this study could provide sleep experts with the method to objectively monitor sleep-disordered breathing in CPAP system or PSG study.

Original languageEnglish (US)
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages6870-6872
Number of pages3
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Country/TerritoryJapan
CityOsaka
Period7/3/137/7/13

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint

Dive into the research topics of 'Automatic snoring detection from nasal pressure data'. Together they form a unique fingerprint.

Cite this