TY - GEN
T1 - Sleep apnea screening with a contact-free under-the-mattress sensor
AU - Davidovich, Maayan L.Yizraeli
AU - Karasik, Roman
AU - Tal, Asher
AU - Shinar, Zvika
N1 - Publisher Copyright:
© 2016 CCAL.
PY - 2016/3/1
Y1 - 2016/3/1
N2 - Sleep apnea is a highly prevalent yet under-diagnosed condition. This study tested a novel algorithm for sleep apnea screening with a contact-free system based on a piezo-electric sensor (PE system - EarlySense Ltd). The study population included 96 subjects who were referred to a sleep study, and underwent a full overnight polysomnography (PSG) in a sleep lab. 16 participants were diagnosed with severe sleep apnea, 18 with moderate, 30 with mild and 32 with no sleep apnea. All subjects were simultaneously measured with the PE system. Respiration waveform was analyzed to extract time and frequency domain features and calculated an internal index for the number of apnea/hypopnea events. It also used an algorithm for sleep wake detection which is described elsewhere. Based on the internal apnea index and the duration of sleep, the system classified the subjects into two groups: one above and one below an Apnea-Hypopnea-Index (AHI) of 15. The classification was compared to a PSG classification of a blinded sleep expert. The novel algorithm detected moderate-to-severe sleep apnea patients with sensitivity of 88% (100% of the severe sleep apnea patients, and 78% of moderate sleep apnea), specificity of 89%, and positive predictive value (PPV) of 81%. These results together with the convenience of being contact-free make the PE system, with the novel algorithm, suitable for apnea screening at home or hospital setups. It may also be usable for long-term monitoring.
AB - Sleep apnea is a highly prevalent yet under-diagnosed condition. This study tested a novel algorithm for sleep apnea screening with a contact-free system based on a piezo-electric sensor (PE system - EarlySense Ltd). The study population included 96 subjects who were referred to a sleep study, and underwent a full overnight polysomnography (PSG) in a sleep lab. 16 participants were diagnosed with severe sleep apnea, 18 with moderate, 30 with mild and 32 with no sleep apnea. All subjects were simultaneously measured with the PE system. Respiration waveform was analyzed to extract time and frequency domain features and calculated an internal index for the number of apnea/hypopnea events. It also used an algorithm for sleep wake detection which is described elsewhere. Based on the internal apnea index and the duration of sleep, the system classified the subjects into two groups: one above and one below an Apnea-Hypopnea-Index (AHI) of 15. The classification was compared to a PSG classification of a blinded sleep expert. The novel algorithm detected moderate-to-severe sleep apnea patients with sensitivity of 88% (100% of the severe sleep apnea patients, and 78% of moderate sleep apnea), specificity of 89%, and positive predictive value (PPV) of 81%. These results together with the convenience of being contact-free make the PE system, with the novel algorithm, suitable for apnea screening at home or hospital setups. It may also be usable for long-term monitoring.
UR - http://www.scopus.com/inward/record.url?scp=85016127522&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85016127522
T3 - Computing in Cardiology
SP - 849
EP - 852
BT - Computing in Cardiology Conference, CinC 2016
A2 - Murray, Alan
PB - IEEE Computer Society
T2 - 43rd Computing in Cardiology Conference, CinC 2016
Y2 - 11 September 2016 through 14 September 2016
ER -