TY - GEN

T1 - Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

AU - Olesen, Alexander Neergaard

AU - Chambon, Stanislas

AU - Thorey, Valentin

AU - Jennum, Poul

AU - Mignot, Emmanuel

AU - Sorensen, Helge B.D.

PY - 2019

Y1 - 2019

N2 - Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.

AB - Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.

U2 - 10.1109/EMBC.2019.8856570

DO - 10.1109/EMBC.2019.8856570

M3 - Article in proceedings

C2 - 31945960

AN - SCOPUS:85077884528

SN - 978-1-5386-1312-2

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

SP - 556

EP - 561

BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

PB - IEEE

T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

Y2 - 23 July 2019 through 27 July 2019

ER -