1.Analysis of imagery motor effective networks based on dynamic partial directed coherence.
Yabing LI ; Songyun XIE ; Zhenning YU ; Xinzhou XIE ; Xu DUAN ; Chang LIU
Journal of Biomedical Engineering 2020;37(1):38-44
The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time-frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 ( = 0.007) and ROI3 ( = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.
2.A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals.
Yujie CUI ; Songyun XIE ; Xinzhou XIE ; Xu DUAN ; Chuanlin GAO
Journal of Biomedical Engineering 2022;39(1):39-46
Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.
Algorithms
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Brain
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Brain-Computer Interfaces
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Electroencephalography/methods*
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Humans
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Principal Component Analysis
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Signal Processing, Computer-Assisted
3.Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China.
Yan MA ; Dong Shan ZHU ; Ren Bo CHEN ; Nan Nan SHI ; Si Hong LIU ; Yi Pin FAN ; Gui Hui WU ; Pu Ye YANG ; Jiang Feng BAI ; Hong CHEN ; Li Ying CHEN ; Qiao FENG ; Tuan Mao GUO ; Yong HOU ; Gui Fen HU ; Xiao Mei HU ; Yun Hong HU ; Jin HUANG ; Qiu Hua HUANG ; Shao Zhen HUANG ; Liang JI ; Hai Hao JIN ; Xiao LEI ; Chun Yan LI ; Min Qing LI ; Qun Tang LI ; Xian Yong LI ; Hong De LIU ; Jin Ping LIU ; Zhang LIU ; Yu Ting MA ; Ya MAO ; Liu Fen MO ; Hui NA ; Jing Wei WANG ; Fang Li SONG ; Sheng SUN ; Dong Ting WANG ; Ming Xuan WANG ; Xiao Yan WANG ; Yin Zhen WANG ; Yu Dong WANG ; Wei WU ; Lan Ping WU ; Yan Hua XIAO ; Hai Jun XIE ; Hong Ming XU ; Shou Fang XU ; Rui Xia XUE ; Chun YANG ; Kai Jun YANG ; Sheng Li YUAN ; Gong Qi ZHANG ; Jin Bo ZHANG ; Lin Song ZHANG ; Shu Sen ZHAO ; Wan Ying ZHAO ; Kai ZHENG ; Ying Chun ZHOU ; Jun Teng ZHU ; Tian Qing ZHU ; Hua Min ZHANG ; Yan Ping WANG ; Yong Yan WANG
Biomedical and Environmental Sciences 2020;33(12):893-905
Objective:
Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear.
Methods:
A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio (
Results:
Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks.
Conclusion
Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.
Adult
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Aged
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COVID-19/virology*
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China/epidemiology*
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Comorbidity
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Female
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Humans
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Male
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Middle Aged
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Retrospective Studies
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Severity of Illness Index
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Treatment Outcome