Signal shape feature for automatic snore and breathing sounds classification.
Snore analysis techniques have recently been developed for sleep studies. Most snore analysis techniques require reliable methods for the automatic classification of snore and breathing sounds in the sound recording. In this study we focus on this problem and propose an automated method to classify snore and breathing sounds based on the novel feature, 'positive/negative amplitude ratio (PNAR)', to measure the shape of the sound signal. The performance of the proposed method was evaluated using snore and breathing recordings (snore: 22,643 episodes and breathing: 4664 episodes) from 40 subjects. Receiver operating characteristic (ROC) analysis showed that the proposed method achieved 0.923 sensitivity with 0.918 specificity for snore and breathing sound classification on test data. PNAR has substantial potential as a feature in the front end of a non-contact snore/breathing-based technology for sleep studies.
Institute of Technology and Science, the University of Tokushima, Japan.
Takahiro
T
Emoto
Masato
M
Kashihara
Udantha R
UR
Abeyratne
Ikuji
I
Kawata
Osamu
O
Jinnouchi
Masatake
M
Akutagawa
Shinsuke
S
Konaka
Yohsuke
Y
Kinouchi
D001185:Artificial Intelligence; D001331:Automation; D005260:Female; D006801:Humans; D008297:Male; D017286:Polysomnography; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis / Q000503:physiopathology; D012913:Snoring / Q000145:classification* / Q000175:diagnosis*