Abstract
Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (EEG) channel for the purpose of building an automated sleep staging system based on the hybrid prediction engine model. The testing results of the model were promising as the classification accuracies were 98.85%, 92.26%, 94.4%, 95.16% and 93.68% for the wake, non-rapid eye movement S1, non-rapid eye movement S2, non-rapid eye movement S3 and rapid eye movement sleep stages, respectively. The overall classification accuracy was 85.18%. We concluded that it might be possible to employ this approach to build an industrial sleep assessment system that reduced the number of channels that affected the sleep quality and the effort excreted by sleep specialists through the process of the sleep scoring.
Highlights
Sleep is defined as a desired state of unconsciousness
The isolated EEG records were used to build and train a decision tree classifier model that distinguish between WK, rapid eye movement (REM), NREMS1, NREMS2 and NREMS3 sleep stages
The REM/NREMS1 confusion was reduced to 27% while the NREMS2/NREMS3 was reduced to 16%
Summary
Sleep is defined as a desired state of unconsciousness. The science of sleep investigation began to catalog the unique and varying texture of this state over the past 75 years. Standard metrics were needed to characterize what could be observed. A consensus for manual sleep assessment has evolved. (2014) A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features. Since this method has been considered the golden standard for sleep assessment
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