Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.
Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.
复制标题
DOI：
10.1016/j.compbiomed.2015.01.012
复制
发表时间：
2015-04
影响因子：
7.7
通讯作者：
Sunderam S
中科院分区：
文献类型：
--
作者：
Yaghouby F;Sunderam S
The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18 to 79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models—specifically Gaussian mixtures and hidden Markov models—are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's K statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations.
登录
查看更多内容
影响因子：
4.6
作者：
Kemp, B;Zwinderman, AH;Oberyé, JJL
通讯作者：
Oberyé, JJL
DOI：
10.1007/s10928-009-9148-2
发表时间：
2010-04-01
影响因子：
2.5
作者：
Bizzotto, Roberto;Zamuner, Stefano;Gomeni, Roberto
通讯作者：
Gomeni, Roberto
影响因子：
1.9
作者：
LANDIS, JR;KOCH, GG
通讯作者：
KOCH, GG
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
影响因子：
5.6
作者：
Magalang, Ulysses J.;Chen, Ning-Hung;Pack, Allan I.
通讯作者：
Pack, Allan I.