Automatic classification of subjects with and without sleep apnea through snoring analysis.
A new method for indirect identification of Sleep Apnea patients through snoring characteristics is proposed. The method uses a logistic regression model which is fed with several time and frequency parameters from snores and their variability. The information is contained in all the snores automatically detected in nocturnal sound recordings. In the validation of the model, subjects are classified with a sensitivity higher than 93% and a specificity between 73% and 88% when all detected snores are used. The model can also be adjusted to obtain 100% specificity with a corresponding sensitivity between 70% and 87%. This results are better than previous reported methods based on snoring analysis, but with a single channel, and are comparable to the classification scores of several portable apnea monitors when evaluated on a similar number of patients. This technique is a promising tool for the screening of snorers, allowing snorers with a low Apnea-Hypopnea Index (AHI<10) to avoid a full-night polysomnographic study at the hospital.
Solà-Soler
Jordi
J
Centre de Recerca en Enginyeria Biomedica, Universitat Politècnica de Catalunya, Pau Gargallo, 5. 08028 Barcelona, Spain. jordi.sola@upc.edu
Jané
Raimon
R
Fiz
José Antonio
JA
Morera
José
J
D001185:Artificial Intelligence; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012135:Respiratory Sounds; D012680:Sensitivity and Specificity; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology; D012913:Snoring / Q000175:diagnosis* / Q000503:physiopathology; D013018:Sound Spectrography / Q000379:methods*