All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome

• Published in: Medical & biological engineering & computing Vol. 50; no. 4; pp. 373 - 381
• Main Authors: Mesquita, J., Solà-Soler, J., Fiz, J. A., Morera, J., Jané, R.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.04.2012; Springer Nature B.V
• Subjects: Acoustics; Adult; Aged; Biomedical and Life Sciences; Biomedical engineering; Biomedical Engineering and Bioengineering; Biomedicine; Body mass index; Computer Applications; Female; Human Physiology; Humans; Imaging; Male; Middle Aged; Original; Original Article; Polysomnography - methods; Radiology; Severity of Illness Index; Signal Processing, Computer-Assisted; Sleep apnea; Sleep Apnea Syndromes - complications; Sleep Apnea Syndromes - diagnosis; Sleep disorders; Snoring - etiology; Sound; Sound Spectrography - methods; Sound waves; Studies; Time Factors; Time series; Spain; Snore sounds; Snore time interval; Sleep apnea
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-012-0885-9

Sleep apnea–hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7–109.9 h −1 ) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores ( p  = 0.0036, AHI cp: 30 h −1 ) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability ( p  = 0.006, AHI cp: 30 h −1 ) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h −1 , respectively. The features proved to be reliable predictors of the subjects’ SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.