Literature DB >> 22407477

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

J Mesquita1, J Solà-Soler, J A Fiz, J Morera, R Jané.   

Abstract

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.

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Mesh:

Year:  2012        PMID: 22407477      PMCID: PMC3314810          DOI: 10.1007/s11517-012-0885-9

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  31 in total

1.  Regular and non regular snore features as markers of SAHS.

Authors:  J Mesquita; J A Fiz; J Sola-Soler; J Morera; R Jane
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  9 in total

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4.  Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept.

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5.  Predicting Obstructive Sleep Apnea with Periodic Snoring Sound Recorded at Home.

Authors:  Anniina Alakuijala; Tapani Salmi
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6.  Nocturnal snoring sound analysis in the diagnosis of obstructive sleep apnea in the Chinese Han population.

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7.  Energy types of snoring sounds in patients with obstructive sleep apnea syndrome: a preliminary observation.

Authors:  Li-Ang Lee; Jen-Fang Yu; Yu-Lun Lo; Yen-Sheng Chen; Ding-Li Wang; Chih-Ming Cho; Yung-Lun Ni; Ning-Hung Chen; Tuan-Jen Fang; Chung-Guei Huang; Hsueh-Yu Li
Journal:  PLoS One       Date:  2012-12-31       Impact factor: 3.240

8.  Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques.

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  9 in total

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