@Article{cmes.2020.08731,
AUTHOR = {Junbiao Liu, Duanpo Wu, Zimeng Wang, Xinyu Jin, Fang Dong, Lurong Jiang, Chenyi Cai},
TITLE = {Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {123},
YEAR = {2020},
NUMBER = {1},
PAGES = {401--426},
URL = {http://www.techscience.com/CMES/v123n1/38499},
ISSN = {1526-1506},
ABSTRACT = {In the field of medical informatics, sleep staging is a challenging and timeconsuming task undertaken by sleep experts. According to the new standard of the
American Academy of Sleep Medicine (AASM), the stages of sleep are divided into
wakefulness (W), rapid eye movement (REM) and non-rapid eye movement (NREM)
which includes three sleep stages (N1, N2 and N3) that describe the depth of sleep.
This study aims to establish an automatic sleep staging algorithm based on the improved
weighted random forest (WRF) and Hidden Markov Model (HMM) using only the features
extracted from double-channel EEG signals. The WRF classification model focuses on
reducing the bias of the imbalance data, while the HMM model focuses on improving the
detection rate of sleep staging through the relationship between adjacent sleep stages. In
particular, the improved weighted RF classification model can increase the recognition rate
of the N1 stage. In addition, the method of removing features with low variance is used
to select meaningful and contributing feature parameters for model training. This is an
innovative content of this paper. The sleep EEG data are first segmented into 30 s epochs,
and the feature parameters of the epoch data are extracted from the double-channel by
applying continuous wavelet packet transform (WPT). Each epoch is then segmented into
29 subepochs of 2 s long with 1 s overlap, and the frequency domain features and statistical
features of each subepoch are extracted. The performance of the proposed method is tested
by evaluating the accuracy (AC), Kappa coefficient, Recall (R), Precision (P) and F1-score
(F1). In the Sleep-EDF database, the overall AC and Kappa coefficient obtained by WRF are 93.20% and 0.890, respectively using the subject-non-independent test. In the 10 sc*
and 10 st* Sleep-EDF Expanded database, the overall AC and Kappa coefficient obtained
by proposed method are 91.97% and 0.874, respectively using the subject-independent
test. The best AC and Kappa coefficient of single subject can reach 96.3% and 0.912,
respectively. Experimental results show that the performance of the proposed method is
competitive with the most current methods and results, and the recognition rate of N1 stage
is significantly improved.},
DOI = {10.32604/cmes.2020.08731}
}