TY - GEN

T1 - Mobile Apnea Screening System for at-home Recording and Analysis of Sleep Apnea Severity.

AU - Bonnesen, Mathias P.

AU - Sorensen, Helge B.D.

AU - Jennum, Poul

PY - 2018

Y1 - 2018

N2 - Obstructive Sleep Apnea (OSA) is a common sleep disorder affecting >10\% of the middle-aged population. The gold standard diagnostic procedure is the Polysomnography (PSG), which is both costly and time consuming. A simple and non-expensive screening therefore would be of great value. This study presents a novel at-home screening method for OSA using a smartphone, a microphone and a modified armband, to measure continuous biological signals during a whole night sleep. A signal-processing algorithm was used to classify the subjects, into classes according to severity of the disorder. The system was validated by conducting a routine sleep study parallel to the data acquisition on a total of 23 subjects. Both binary and 4-class classification problems were tested. The binary classifications showed the best results with sensitiv- ities between 92.3 % and 100 %, and accuracies between 78.3 % and 91.3 %. The 4-class classification was not as successful with a sensitivity of 75 %, and accuracies of 56.5 % and 60 %. We conclude that mobile smartphone technology has a potential for OSA ambulatory screening.

AB - Obstructive Sleep Apnea (OSA) is a common sleep disorder affecting >10\% of the middle-aged population. The gold standard diagnostic procedure is the Polysomnography (PSG), which is both costly and time consuming. A simple and non-expensive screening therefore would be of great value. This study presents a novel at-home screening method for OSA using a smartphone, a microphone and a modified armband, to measure continuous biological signals during a whole night sleep. A signal-processing algorithm was used to classify the subjects, into classes according to severity of the disorder. The system was validated by conducting a routine sleep study parallel to the data acquisition on a total of 23 subjects. Both binary and 4-class classification problems were tested. The binary classifications showed the best results with sensitiv- ities between 92.3 % and 100 %, and accuracies between 78.3 % and 91.3 %. The 4-class classification was not as successful with a sensitivity of 75 %, and accuracies of 56.5 % and 60 %. We conclude that mobile smartphone technology has a potential for OSA ambulatory screening.

U2 - 10.1109/EMBC.2018.8512335

DO - 10.1109/EMBC.2018.8512335

M3 - Article in proceedings

C2 - 30440433

AN - SCOPUS:85056666178

SN - 978-1-5386-3647-3

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 457

EP - 460

BT - 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

PB - IEEE

T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

Y2 - 18 July 2018 through 21 July 2018

ER -