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作者： Zihan Chen Yunjie Fang Wei Yan Jun Wang Jin Li Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University College of Physics and Information Technology Shaanxi Normal University Key Laboratory of Biomedical Functional Materials China Pharmaceutical University

Transfer entropy（TE） has been broadly used in the field of *** this paper,the partial information decomposition algorithm is employed to decompose multivariate TE into synergistic,redundant and unique *** this work,the synergistic part is believed as more suitable as the computation *** recorded the magnetoencephalogram（MEG） data of 6 subjects with depression and 13 normal subjects under different emotional stimulations,and studied the coupling between multiple symmetric channels in the frontal area in the brain of *** experimental results show that under different emotional stimulations,normal people present significant difference from the depression patients,especially in the right frontal ***,under negative emotional stimulation,the difference in synergistic value between normal people and depression patients is *** synergistic value of depression patients has become bigger,which indicates that the brain complexity of depression patients has grown,and their brain activities have increased.

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Amplitudes and frequencies cross modulation of epileptic EEG between 16 leads 3

Amplitudes and frequencies cross modulation of epileptic EEG...

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3rd International Conference on Biological Information and Biomedical engineering, BIBE 2019

作者： Cai, Yujie Dai, Jiafei Li, Jin Wang, Jun Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

ISBN: (纸本)9783800750269

Epilepsy is caused by abnormal brain nerves. In order to analyze the similarities and differences of EEG signals between patients with epilepsy and normal people, this paper introduced a cross modulation algorithm. This paper analyzed the correlation between amplitude and frequency between EEG signals in various parts of the brain. The approach overcomes the limitation to complexity of the algorithm and found abnormalities in EEG signals of temporal lobe and occipital lobe. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Computational complexity

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Cross-Modulated Amplitudes and Frequencies of Epileptic EEG 4

Cross-Modulated Amplitudes and Frequencies of Epileptic EEG

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Cai, Yujie Dai, Jiafei Li, Jin Wang, Jun Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

Epilepsy is a neural disorder with the hallmark of recurrent seizures. To characterize the epileptic brain electrical activities, we employed the cross modulation of instantaneous amplitudes and frequencies to separate synchronous and anti-synchronous modulation. Amplitude-amplitude, amplitude-frequency and frequency-frequency cross modulation were adopted to analyse the difference between EEG signal of epileptic patients and that of normal people. By comparing the observed patterns with two groups of EEG signals we demonstrate that the cross-modulation exponentials at the temporal region and the occipital region of right hemisphere are significant different. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Frequency modulation

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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms 12

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Zhong, Weiyang Jiang, Shanxi Wu, Wei Peng, Danhong Xu, Tian Wang, Jun Wang, Gongdao Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China China Tower Corporation Limited Nantong Branch China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing210009 China Department of Obstetrics and Gynecology Zhongda Hospital Southeast University Nanjing School of Science China Pharmaceutical University Nanjing China

ISBN: (纸本)9781728148526

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network. © 2019 IEEE.

关键词： Genetic algorithms

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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Weiyang Zhong Shanxi Jiang Wei Wu Danhong Peng Tian Xu Jun Wang Gongdao Wang Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing PR China China Tower Corporation Limited Nantong Branch PR China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing PR China Zhongda Hospital Southeast University NanjingSchool of Science China Pharmaceutical University Nanjing PR China

关键词： Classification algorithms Training Neural networks Diabetes Pregnancy Genetic algorithms Sociology

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Epileptic brain network analysis based on Kendall's improved synchronization algorithm 1

Epileptic brain network analysis based on Kendall's improved...

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1st International Conference on Physics, Mathematics and Statistics, ICPMS 2018

作者： Ding, Chuchu Sun, Ting Zou, Xin Dai, Jiafei Li, Jin Wang, Jun Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing210002 China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing210009 China

In this paper, we use a new algorithm, the IRC algorithm, to improve the Kendall algorithm. Research on complex networks has gradually deepened into all areas of social science. The study of brain networks has become a hot topic in the study of brain function. The method of wavelet filtering is used to filter the EEG data to obtain the required α-band (8-16 Hz). Using the improved IRC algorithm, the brain functional network is constructed based on the EEG data, and the related characteristics of the brain network constructed are analyzed. The experimental results show that the method is suitable for distinguishing the network degree indicators of epilepsy and normal brain tissue, and further deepening the study of the neurokinetic behavior of the brain. © Published under licence by IOP Publishing Ltd.

关键词： Complex networks

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Multilingual Focused Crawler System based on Web Content Extraction and Path Configuration

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IOP Conference Series: Materials Science and engineering 2019年第5期569卷

作者： Jie Wang Sanhong Deng Lijuan Wang School of Information Management Nanjing University Nanjing 210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University) Nanjing 210023 China Geological Survey of Jiangsu Province Nanjing 210018 China

The multilingual focused crawler system combines web content extraction with path configuration to make use of their advantages and achieve automatic collection of network information in multiple languages. Firstly, system selects foreign language keywords according to crawling webpage language and Chinese keywords, and uses initial link to obtain webpage information. Then, it uses path configuration information or web content extraction algorithm based on the distribution line block to get webpage content, and adopts rules or configuration information to acquire new links, published time and title. Next, keywords are used to filter irrelevant information. Finally, results are presented as a list. When users use focused crawler system, the webpage path information can be configured or not according to requirements, and the collected network resources can also be searched or filtered.

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Research on the MEG of depression patients based on multivariate TE partial information decomposition

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IOP Conference Series: Materials Science and engineering 2020年第5期768卷

作者： Zihan Chen Yunjie Fang Wei Yan Jun Wang Jin Li Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing 210009 China

Transfer entropy (TE) has been broadly used in the field of neurosciences. In this paper, the partial information decomposition algorithm is employed to decompose multivariate TE into synergistic, redundant and unique parts. In this work, the synergistic part is believed as more suitable as the computation method. We recorded the magnetoencephalogram (MEG) data of 6 subjects with depression and 13 normal subjects under different emotional stimulations, and studied the coupling between multiple symmetric channels in the frontal area in the brain of subject. The experimental results show that under different emotional stimulations, normal people present significant difference from the depression patients, especially in the right frontal area. Furthermore, under negative emotional stimulation, the difference in synergistic value between normal people and depression patients is smaller. The synergistic value of depression patients has become bigger, which indicates that the brain complexity of depression patients has grown, and their brain activities have increased.

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Epileptic Left-right Brain Network Analysis Based on Kendall's Improved Synchronization Algorithm

Epileptic Left-right Brain Network Analysis Based on Kendall...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Xin Zou Ting Sun Chuchu Ding Jiafei Dai Jin Li Jun Wang Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi’an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

ISBN: (纸本)9781538657393;9781538657386

Complex networks can be regarded as an abstraction of the description of a complex system. This paper presents an improved nonlinear synchronization algorithm IRC (inverse rank correlation) based on Kendall rank correlation. The IRC coupling coefficient is asymmetric. The brain function network has directionality by constructing with the IRC coupling coefficient matrices, which is helpful to study the nonlinear dynamic behavior of the brain network. The IRC algorithm was used to construct the left and right brain functional networks based on multi-channel EEG data, and the average degree index of the regional brain function network was analyzed to study the similarities and differences of regional brain networks between epilepsy patients and normal people. The results show that the improved algorithm can significantly distinguish between epilepsy and normal left brain region functional networks, in which the complexity of the epilepsy and the normal left brain are different. The experimental data show that the analysis of IRC network will help the clinical diagnosis and analysis of epilepsy, further deepen the study of the neurological dynamics of the brain, and provide an effective tool for clinical diagnosis.

关键词： Epilepsy Correlation Electroencephalography Couplings Mathematical model Correlation coefficient Complexity theory

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Analysis of Epilepsy EEG and ECG Correlation Based on IRC Algorithm

Analysis of Epilepsy EEG and ECG Correlation Based on IRC Al...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Ting Sun Xin Zou Chuchu Ding Jiafei Dai Jin Li Jun Wang Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi’an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

ISBN: (纸本)9781538657393;9781538657386

In this paper, the correlation analysis of EEG signals and ECG signals between epileptic and normal population was carried out based on the improved synchronization algorithm IRC based on Kendall. In clinical animal experiments, it was found that when the epilepsy sample attacks, irregular heart rate changes often occur. In this experiment, an improved IRC algorithm was used to analyze the correlation between EEG signals and ECG signals to study whether the correlation between epilepsy patients was different from that of normal people. Experimental results show that using the IRC algorithm based on Kendall improvement can significantly distinguish the correlation between EEG signals and ECG signals in the left forehead and left anterior temporal regions of normal and epileptic populations, and further study on the correlation between brain and heart in epileptic patients.

关键词： Electroencephalography Electrocardiography Correlation Epilepsy Correlation coefficient Electrodes Forehead

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