Fengzhen Hou

Epileptic brain network analysis based on Kendall’s improved synchronization algorithm

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.

Analysis of Alpha Rhythm Electroencephalogram Based on Inner Composition Alignment Algorithm

To research on brain function network, we must firstly figure out the structure of the brain. Brain connection diagram can be drawn up on the levels of macro and microscopic scale. This paper applied inner composition alignment (iota) algorithm to construct alpha rhythm brain functional network and visualize the network topology and analysis and the difference between the old and young’s brain functional connectivity. The results show that clustering coefficient of the old brain network obviously differs from the young by calculating t testing with spss software, which proved that clustering coefficient of the old alpha rhythm, differs from the young.

Analysis of multiscale sign series entropy of the young and middle-aged electroencephalogram signals

The physiological analysis of electroencephalogram (EEG) signals is of great significance in assessing the activity of the brain function and the physiological state. EEG is a means of clinical examination of brain diseases. Age is one of the important factors that affect the results of the EEG. EEG signal analysis is mainly to analyze the time series of the signal, multiscale entropy (MSE) analysis [1-3] is the method that used to analyze the finite length of the time series. Multiscale sign series entropy (MSSE) method is proposed for the analysis of EEG signals in the young and middle-aged. We use the proposed method to analyze the signals from several aspects of data length, word length, noise, multi scale etc. By analyzing the influence of these factors, we can still distinguish the EEG signals of different ages. Multiscale sign series entropy (MSSE) analysis algorithm can effectively separate the brain electrical signals from the young and middle aged, which is expected to have a certain reference value for the traditional pathological analysis of the EEG signals.

Electroencephalogram signal analysis based on the improved k-nearest neighbor network

In this paper, we propose a method to analyze epileptic electroencephalogram based on time series that is transformed from improved k-nearest neighbor network. The study of complex networks has become a hot research of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so researching the time series from the network can also achieve the purpose of studying epileptic electroencephalogram. The results of this experiment show that studying power spectrum of time series from the network is more easily than the power spectrum of time series directly generated from brain data to distinguish between normal and epileptic patients. In addition, studying the clustering coefficient of improved k-nearest neighbor network is also able to distinguish between normal and patients with epilepsy. This study can provide an important reference for the study of epilepsy and clinical diagnosis.