Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model

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.

Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG

The grading of hypoxic-ischemic encephalopathy(HIE)contributes to the clinical decision making for neonates with HIE.In this paper,an automated grading method based on electroencephalogram(EEG)data is proposed to describe the severity of HIE infants,namely mild asphyxia,moderate asphyxia and severe asphyxia.The automated grading method is based on a multi-class support vector machine(SVM)classifier,and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch data with 4 s overlap.Of note,the correlation coefficient and asymmetry extracted in this paper have obvious discriminating capability in HIE infants classification.The experimental results show that the proposed method can achieve the classification accuracy of 78.3%,75.8%and 87.0%of the mild asphyxia group,moderate asphyxia group and severe asphyxia group,respectively.Moreover,the overall accuracy and kappa used to evaluate the performance of the proposed method can reach 79.5%and 0.69,respectively.