TY - JOUR

T1 - Novel method for detection of Sleep Apnoea using respiration signals

AU - Carmes, Kristine

AU - Kempfner, Lykke

AU - Sorensen, Helge Bjarup Dissing

AU - Jennum, Poul

PY - 2014

Y1 - 2014

N2 - Polysomnography (PSG) studies are considered the "gold standard" for the diagnosis of Sleep Apnoea (SA). Identifying cessations of breathing from long-lasting PSG recordings manually is a labour-intensive and time-consuming task for sleep specialist, associated with inter-scorer variability. In this study a simplified, semi-automatic, three-channel method for detection of SA patients is proposed in order to increase analysis reliability and diagnostic accuracy in the clinic. The method is based on characteristic features, such as respiration stoppages pr. hour and the total number of oxygen desaturations > 3%, extracted from the thorax and abdomen respiration effort belts, and the oxyhemoglobin saturation (SaO2), fed to an Elastic Net classifier and validated according to American Academy of Sleep Medicine (AASM) using the patients' AHI value. The method was applied to 109 patient recordings and resulted in a very high SA classification with accuracy of 97.9%. The proposed method reduce the time spent on manual analysis of respiration stoppages and the inter- and intra-scorer variability, and may serve as an alternative screening method for SA.

AB - Polysomnography (PSG) studies are considered the "gold standard" for the diagnosis of Sleep Apnoea (SA). Identifying cessations of breathing from long-lasting PSG recordings manually is a labour-intensive and time-consuming task for sleep specialist, associated with inter-scorer variability. In this study a simplified, semi-automatic, three-channel method for detection of SA patients is proposed in order to increase analysis reliability and diagnostic accuracy in the clinic. The method is based on characteristic features, such as respiration stoppages pr. hour and the total number of oxygen desaturations > 3%, extracted from the thorax and abdomen respiration effort belts, and the oxyhemoglobin saturation (SaO2), fed to an Elastic Net classifier and validated according to American Academy of Sleep Medicine (AASM) using the patients' AHI value. The method was applied to 109 patient recordings and resulted in a very high SA classification with accuracy of 97.9%. The proposed method reduce the time spent on manual analysis of respiration stoppages and the inter- and intra-scorer variability, and may serve as an alternative screening method for SA.

U2 - 10.1109/EMBC.2014.6943578

DO - 10.1109/EMBC.2014.6943578

M3 - Journal article

C2 - 25569946

VL - 2014

SP - 258

EP - 261

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SN - 0589-1019

ER -