+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

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



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



Physiological Measurement 34(5): N41-N49



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.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 055824762

Download citation: RISBibTeXText

PMID: 23587724

DOI: 10.1088/0967-3334/34/5/n41


Related references

Polyvinylidene fluoride sensor-based method for unconstrained snoring detection. Physiological Measurement 36(7): 1399-1414, 2016

A simple procedure for quantitative and time coded detection of snoring sounds in apnea and snoring patients. Laryngologie, Rhinologie, Otologie 67(9): 449-452, 1988

Reliability of home-based physiological sleep measurements in snoring and non-snoring 3-year olds. Sleep and Breathing 17(1): 147-156, 2013

Gene detection based on hidden Markov models. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, August, 2002 Session 16: 1-7, 2002

A method for extracting temporal parameters based on hidden Markov models in body sensor networks with inertial sensors. IEEE Transactions on Information Technology in Biomedicine 13(6): 1019-1030, 2010

Online apnea-bradycardia detection based on hidden semi-Markov models. Medical and Biological Engineering and Computing 53(1): 1-13, 2015

Radiocephalometric findings and duration of snoring in habitual snoring and obstructive apnea syndrome. Laryngo- Rhino- Otologie 68(3): 163-168, 1989

Does laser-assisted palatoplasty reduce snoring index or snoring sound level?. Thorax 55(Suppl. 3): A54, 2000

Recurrent Neural Network for Classification of Snoring and Non-Snoring Sound Events. Conference Proceedings 2018: 328-331, 2018

Snoring and Stroke a Case-Control Study with Objective Measurement of Snoring. Age and Ageing 26(Suppl 3): P5-P5, 1997

Sleep hygiene and problem behaviors in snoring and non-snoring school-age children. Sleep Medicine 13(7): 802-809, 2012

Robust remote homology detection by feature based Profile Hidden Markov Models. Statistical Applications in Genetics and Molecular Biology 4: Article21-Article21, 2006

Parental reports showed that snoring in infants at three and eight months associated with snoring parents and smoking mothers. Acta Paediatrica 2019, 2019

Patients' and cohabitants' reports on snoring and daytime sleepiness, 1-8 years after surgical treatment of snoring. Orl; Journal for Oto-Rhino-Laryngology and Its Related Specialties 61(1): 19-24, 1999

The snoring spectrum: acoustic assessment of snoring sound intensity in 1,139 individuals undergoing polysomnography. Chest 115(3): 762-770, 1999