TY - JOUR
T1 - Snoring detection using a piezo snoring sensor based on hidden Markov models
AU - Lee, Hyo Ki
AU - Lee, Jeon
AU - Kim, Hojoong
AU - Ha, Jin Young
AU - Lee, Kyoung Joung
PY - 2013/5
Y1 - 2013/5
N2 - 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.
AB - 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.
KW - hidden Markov models
KW - piezo snoring sensor
KW - snoring detection
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U2 - 10.1088/0967-3334/34/5/N41
DO - 10.1088/0967-3334/34/5/N41
M3 - Article
C2 - 23587724
AN - SCOPUS:84876945638
VL - 34
SP - N41-N49
JO - Clinical Physics and Physiological Measurement
JF - Clinical Physics and Physiological Measurement
SN - 0967-3334
IS - 5
ER -