TY - GEN
T1 - Breath-by-breath detection of apneic events for OSA severity estimation using non-contact audio recordings
AU - Rosenwein, T.
AU - Dafna, E.
AU - Tarasiuk, A.
AU - Zigel, Y.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Obstructive sleep apnea (OSA) is a prevalent sleep disorder, characterized by recurrent episodes of upper airway obstructions during sleep. We hypothesize that breath-by-breath audio analysis of the respiratory cycle (i.e., inspiration and expiration phases) during sleep can reliably estimate the apnea hypopnea index (AHI), a measure of OSA severity. The AHI is calculated as the average number of apnea (A)/hypopnea (H) events per hour of sleep. Audio signals recordings of 186 adults referred to OSA diagnosis were acquired in-laboratory and at-home conditions during polysomnography and WatchPat study, respectively. A/H events were automatically segmented and classified using a binary random forest classifier. Total accuracy rate of 86.3% and an agreement of κ=42.98% were achieved in A/H event detection. Correlation of r=0.87 (r=0.74), diagnostic agreement of 76% (81.7%), and average absolute difference AHI error of 7.4 (7.8) (events/hour) were achieved in in-laboratory (at-home) conditions, respectively. Here we provide evidence that A/H events can be reliably detected at their exact time locations during sleep using non-contact audio approach. This study highlights the potential of this approach to reliably evaluate AHI in at home conditions.
AB - Obstructive sleep apnea (OSA) is a prevalent sleep disorder, characterized by recurrent episodes of upper airway obstructions during sleep. We hypothesize that breath-by-breath audio analysis of the respiratory cycle (i.e., inspiration and expiration phases) during sleep can reliably estimate the apnea hypopnea index (AHI), a measure of OSA severity. The AHI is calculated as the average number of apnea (A)/hypopnea (H) events per hour of sleep. Audio signals recordings of 186 adults referred to OSA diagnosis were acquired in-laboratory and at-home conditions during polysomnography and WatchPat study, respectively. A/H events were automatically segmented and classified using a binary random forest classifier. Total accuracy rate of 86.3% and an agreement of κ=42.98% were achieved in A/H event detection. Correlation of r=0.87 (r=0.74), diagnostic agreement of 76% (81.7%), and average absolute difference AHI error of 7.4 (7.8) (events/hour) were achieved in in-laboratory (at-home) conditions, respectively. Here we provide evidence that A/H events can be reliably detected at their exact time locations during sleep using non-contact audio approach. This study highlights the potential of this approach to reliably evaluate AHI in at home conditions.
KW - OSA
KW - audio signal processing
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=84953212969&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2015.7320173
DO - 10.1109/EMBC.2015.7320173
M3 - Conference contribution
C2 - 26738073
AN - SCOPUS:84953212969
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7688
EP - 7691
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -