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您的检索式：作者名="Yinxing Wan"
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| 1 | Recent Advances in Fatigue Detection Algorithm Based on EEG显示文摘Fatigue is a state commonly caused by overworked,which seriously affects daily work and life.How to detect mental fatigue has always been a hot spot for researchers to explore.Electroencephalogram(EEG)is considered one of the most accurate and objective indicators.This article investigated the devel-opment of classification algorithms applied in EEG-based fatigue detection in recent years.According to the different source of the data,we can divide these classification algorithms into two categories,intra-subject(within the same sub-ject)and cross-subject(across different subjects).In most studies,traditional machine learning algorithms with artificial feature extraction methods were com-monly used for fatigue detection as intra-subject algorithms.Besides,deep learn-ing algorithms have been applied to fatigue detection and could achieve effective result based on large-scale dataset.However,it is difficult to perform long-term calibration training on the subjects in practical applications.With the lack of large samples,transfer learning algorithms as a cross-subject algorithm could promote the practical application of fatigue detection methods.We found that the research based on deep learning and transfer learning has gradually increased in recent years.But as afield with increasing requirements,researchers still need to con-tinue to explore efficient decoding algorithms,design effective experimental para-digms,and collect and accumulate valid standard data,to achieve fast and accurate fatigue detection methods or systems to further widely apply. | Fei Wang Yinxing Wan Man Li Haiyun Huang Li Li Xueying Hou Jiahui Pan Zhenfu Wen Jingcong Li | 2023 | Intelligent Automation & Soft Computing2023，，3： | 1 |
| 2 | An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost显示文摘Sleep spindles are an electroencephalogram(EEG)biomarker of non-rapid eye movement(NREM)sleep and have important implications for clinical diagnosis and prognosis.However,it is challenging to accurately detect sleep spindles due to the complexity of the human brain and the uncertainty of neural mechanisms.To improve the reliability and objectivity of sleep spindle detection and to compensate for the limitations of manual annotation,this study proposes a new automatic detection algorithm based on Matching Pursuit(MP)and Least Squares Boosting(LSBoost),where the automatic sleep spindle detection algorithm can help reduce the visual annotation workload of sleep clinicians.Specifically,MP is a time-frequency analysis method suitable for extracting spindle wave characteristics,which can accurately locate spindle waves on a time-frequency plane.LSBoost is an ensemble learning classification method to deal with unbalanced data.Initially,theMP method is used to search for EEG segments that are possible spindle waves from the filtered raw EEG data.Then,the designed feature segments are thrown into the LSBoost classifier to further identify the real spindles from all candidates and output the final results.The proposed method is verified on the common public dataset DREAMS.The experiment results showthat the sensitivity and F1-scores based on the sample-based assessments achieve 68.2% and 55.4%,respectively.Furthermore,the Recall and F1-score based on the event assessments are 83.8% and 70.8%,respectively.These results show that the proposed algorithm is robust to the subject changes in the DREAMS dataset.In addition,it improves the quality of sleep spindle detection,which is expected to assist the manual marking of experts. | Fei Wang Li Li Yinxing Wan Zhuorong Li Lixian Luo Bangshun Hu Jiahui Pan Zhenfu Wen Haiyun Huang | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
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