TY - GEN
T1 - Automatic event detection of REM sleep without atonia from polysomnography signals using deep neural networks
AU - Wallis, Phillip
AU - Yaeger, Daniel
AU - Kain, Alexander
AU - Song, Xubo
AU - Lim, Miranda
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020/5
Y1 - 2020/5
N2 - Rapid eye movement (REM) sleep behavior disorder (RBD) is a sleep disorder that features loss of atonia, or REM sleep without atonia (RSWA). RBD and RSWA are early manifestations of degenerative neurological diseases such as Parkinson's and Lewy Body Dementia. Accurate diagnosis of RBD is crucial for proper treatment planning and is invaluable for early detection of these neurodegenerative diseases. The current gold standard diagnosis of RSWA is through manual visual scoring by a clinician, which is labor-intensive, costly and error-prone. We develop a novel, efficient, and objective method using deep learning to detect RSWA events from polysomnography signals using a large cohort of 692 patients. Unlike previous automated methods that generate only a binary patient diagnosis, our method detects the location and class of all RSWA events. This finer-grained analysis forms the basis for subsequent diagnosis, and allows the quantification of event duration and frequency which in turn can help quantify disease load.
AB - Rapid eye movement (REM) sleep behavior disorder (RBD) is a sleep disorder that features loss of atonia, or REM sleep without atonia (RSWA). RBD and RSWA are early manifestations of degenerative neurological diseases such as Parkinson's and Lewy Body Dementia. Accurate diagnosis of RBD is crucial for proper treatment planning and is invaluable for early detection of these neurodegenerative diseases. The current gold standard diagnosis of RSWA is through manual visual scoring by a clinician, which is labor-intensive, costly and error-prone. We develop a novel, efficient, and objective method using deep learning to detect RSWA events from polysomnography signals using a large cohort of 692 patients. Unlike previous automated methods that generate only a binary patient diagnosis, our method detects the location and class of all RSWA events. This finer-grained analysis forms the basis for subsequent diagnosis, and allows the quantification of event duration and frequency which in turn can help quantify disease load.
KW - Deep Learning
KW - Machine Learning
KW - Polysomnography (PSG)
KW - REM sleep without atonia (RSWA)
KW - Rapid eye movement sleep behavior disorder (RBD)
UR - http://www.scopus.com/inward/record.url?scp=85091153985&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091153985&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9054506
DO - 10.1109/ICASSP40776.2020.9054506
M3 - Conference contribution
AN - SCOPUS:85091153985
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4112
EP - 4116
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -