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您的检索式：作者名="Junbiao Fang"
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| 1 | Blood pressure levels and prognosis of intracranial trauma patients with cognitive dysfunc- tion显示文摘 | Weiyu Wang Junbiao Fang Bing Lei | 2014 | Pak J Med Sci2014，30，4： | 1 |
| 2 | Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model显示文摘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. | Junbiao Liu Duanpo Wu Zimeng Wang Xinyu Jin Fang Dong Lurong Jiang Chenyi Cai | 2020 | Computer Modeling in Engineering & Sciences2020，，4： | 0 |
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