TY - GEN
T1 - A Novel Sleep Stage Scoring System
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
AU - Gunnarsdottir, Kristin M.
AU - Gamaldo, Charlene E.
AU - Salas, Rachel M.E.
AU - Ewen, Joshua B.
AU - Allen, Richard P.
AU - Sarma, Sridevi V.
N1 - Funding Information:
*Research supported by The Johns Hopkins Catalyst Award; JHU CFAR NIH/NIAID fund 1P30AI094189-01A1; NSF 1609038. K. M. Gunnarsdottir and S. V. Sarma are with Johns Hopkins University, Baltimore, MD, USA (correspondence e-mail: kgunnar1@jhu.edu). C. E. Gamaldo, R. M. E. Salas, J. B. Ewen and R. P. Allen are with Johns Hopkins School of Medicine, Baltimore, MD, USA.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Overnight polysomnography (PSG) is the gold standard tool used to characterize sleep and for diagnosing sleep disorders. PSG is a non-invasive procedure that collects various physiological data which is then scored by sleep specialists who assign a sleep stage to every 30-second window of the data according to predefined scoring rules. In this study, we aimed to automate the process of sleep stage scoring of overnight PSG data while adhering to expert-based rules. We developed an algorithm utilizing a likelihood ratio decision tree classifier and extracted features from EEG, EMG and EOG signals based on predefined rules of the American Academy of Sleep Medicine Manual. Specifically, features were computed in 30-second epochs in the time and the frequency domains of the signals and used as inputs to the classifier which assigned each epoch to one of five possible stages: N3, N2, N1, REM or Wake. The algorithm was trained and tested on PSG data from 38 healthy individuals with no reported sleep disturbances. The overall scoring accuracy was 80.70% on the test set, which was comparable to the training set. Our results imply that the automatic classification is highly robust, fast, consistent with visual scoring and is highly interpretable.
AB - Overnight polysomnography (PSG) is the gold standard tool used to characterize sleep and for diagnosing sleep disorders. PSG is a non-invasive procedure that collects various physiological data which is then scored by sleep specialists who assign a sleep stage to every 30-second window of the data according to predefined scoring rules. In this study, we aimed to automate the process of sleep stage scoring of overnight PSG data while adhering to expert-based rules. We developed an algorithm utilizing a likelihood ratio decision tree classifier and extracted features from EEG, EMG and EOG signals based on predefined rules of the American Academy of Sleep Medicine Manual. Specifically, features were computed in 30-second epochs in the time and the frequency domains of the signals and used as inputs to the classifier which assigned each epoch to one of five possible stages: N3, N2, N1, REM or Wake. The algorithm was trained and tested on PSG data from 38 healthy individuals with no reported sleep disturbances. The overall scoring accuracy was 80.70% on the test set, which was comparable to the training set. Our results imply that the automatic classification is highly robust, fast, consistent with visual scoring and is highly interpretable.
UR - http://www.scopus.com/inward/record.url?scp=85056657829&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056657829&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513039
DO - 10.1109/EMBC.2018.8513039
M3 - Conference contribution
C2 - 30441082
AN - SCOPUS:85056657829
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3240
EP - 3243
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 July 2018 through 21 July 2018
ER -