Large-Scale Automated Sleep Staging.

ABSTRACT:

Study objectives

Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain. The objectives are to investigate the extent to which machine learning methods can approximate the performance of human scorers when supplied with sufficient training cases and to investigate how staging performance depends on the number of training patients, contextual information, model complexity, and imbalance between sleep stage proportions.

Methods

A total of 102 features were extracted from six electroencephalography (EEG) channels in routine polysomnography. Two thousand nights were partition

SUBMITTER: Sun H 

PROVIDER: S-EPMC6251659 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature