Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults

Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults. Ben-Israel N, et al  Sleep. 2012 September 1; 35(9): 1299–1305. doi: 10.5665/sleep.2092

Title: Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults

STUDY OBJECTIVE:  To develop a whole-night snore sounds analysis algorithm enabling estimation of obstructive apnea hypopnea index (AHI(EST)) among adult subjects.

Obstructive apnea and snoring analysisDESIGN: Snore sounds were recorded using a directional condenser microphone placed 1 m above the bed. Acoustic features exploring intra-(mel- cepstability, pitch density) and inter-(running variance, apnea phase ratio, inter-event silence) snore properties were extracted and integrated to assess AHI(EST).

SETTING: University-affiliated sleep-wake disorder center and biomedical signal processing laboratory.

PATIENTS: Ninety subjects (age 53 ± 13 years, BMI 31 ± 5 kg/m(2)) referred for polysomnography (PSG) diagnosis of OSA were prospectively and consecutively recruited. The system was trained and tested on 60 subjects. Validation was blindly performed on the additional 30 consecutive subjects.

MEASUREMENTS AND RESULTS: AHI(EST) correlated with AHI (AHI(PSG); r(2) = 0.81, P < 0.001). Area under the receiver operating characteristic curve of 85% and 92% for thresholds of 10 and 20 events/h, respectively, were obtained for OSA detection. Both Altman-Bland analysis and diagnostic agreement criteria revealed 80% and 83% agreements of AHI(EST) with AHI(PSG), respectively.

CONCLUSIONS: Acoustic analysis based on intra- and inter-snore properties can differentiate subjects according to AHI. An acoustic-based screening system may address the growing needs for reliable OSA screening tool. Further studies are needed to support these findings.

page-dividerCOMMENT: Obstructive apnea hypopnea index estimation by analysis of snoring signals in adults.Many readers may have been able to ‘perform’ qualitative aspects of this study in any given tramping hut. The Israeli authors carefully analysed five acoustic features of snoring: a) entire night stability; b) intersnore variability across the night; c) apnoeic-phase ratio; d) inter-event silence count; and e) pitch density, as a measure of tissue vibration frequency. They derived a computer model using information gained from the simple microphone above the bed to predict the presence and severity of sleep apnoea. Bottom line: this acoustic-based system may assist dealing with the flood of undiagnosed sleep apnoea patients. (Associate Professor Lutz Beckert – Respiratory Research Review)

Sleep Doctor research commentary website provided by Sleep Well Clinic.


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