Snoring detection using a piezo snoring sensor based on hidden Markov models

Hyo Ki Lee, Jeon Lee, Hojoong Kim, Jin Young Ha, Kyoung Joung Lee

Research output: Contribution to journalArticlepeer-review

Abstract

This study presents a snoring detection method based on hidden Markov models (HMMs) using a piezo snoring sensor. Snoring is a major symptom of obstructive sleep apnea (OSA). In most sleep studies, snoring is detected with a microphone. Since these studies analyze the acoustic properties of snoring, they need to acquire data at high sampling rates, so a large amount of data should be processed. Recently, several sleep studies have monitored snoring using a piezo snoring sensor. However, an automatic method for snoring detection using a piezo snoring sensor has not been reported in the literature. This study proposed the HMM-based method to detect snoring using this sensor, which is attached to the neck. The data from 21 patients with OSA were gathered for training and test sets. The short-time Fourier transform and short-time energy were computed so they could be applied to HMMs. The data were classified as snoring, noise and silence according to their HMMs. As a result, the sensitivity and the positive predictivity values were 93.3% and 99.1% for snoring detection, respectively. The results demonstrated that the method produced simple, portable and user-friendly detection tools that provide an alternative to the microphone-based method.

Original languageEnglish (US)
Pages (from-to)N41-N49
JournalPhysiological Measurement
Volume34
Issue number5
DOIs
StatePublished - May 2013
Externally publishedYes

Keywords

  • hidden Markov models
  • piezo snoring sensor
  • snoring detection

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

Fingerprint

Dive into the research topics of 'Snoring detection using a piezo snoring sensor based on hidden Markov models'. Together they form a unique fingerprint.

Cite this