Biometric identification of persons using sample entropy features of EEG during rest state

2016 ◽  
Author(s):  
Kavitha P. Thomas ◽  
A. P. Vinod
Keyword(s):  
Sample Entropy ◽  
Biometric Identification ◽  
Rest State ◽  
Identification Of Persons

scholarly journals Hand contour classification using evolutionary algorithm

2020 ◽  
Vol 49 (1) ◽  
pp. 55-79
Author(s):  
Jerry Moravec
Keyword(s):  
Linear Regression ◽  
Evolutionary Algorithm ◽  
Iterative Closest Point ◽  
Biometric Identification ◽  
Human Hand ◽  
Topological Description ◽  
Computer Classification ◽  
Combination Of Methods ◽  
Identification Of Persons ◽  
The Given

A biometric identification of persons wchich utilize contour of a human hand belogs to still very interesting and still not totally explored areas and its accuracy and effectiveness depends on technical capabilities to some extent. Presented paper solves given problem using combination of different algorithms. A hand contour is used, topological description of the hand, evolutionary algorithm, algorithm linear regression to estimate the knuckles positions and for contours comparison is used an algorithm Iterative Closest Point (ICP) in its genuine shape. All 5 fingers is at computer classification fully moveable, thumb has 2 knuckles. Modern evolutionary optimizers enable markedly to cut down computational demands of the algorithm ICP. Experimental verification of proposed recipes were performed with use of two different databases named THID and GPDS with persons of both gender and different age (cca 20-65let) with total number of oeprons in individual database 104 and 94. Experimental results checked succesfuly suitability of use combination of methods ICP and evolutionary optimizer which is named as EPSDE for solving of the given task with algorithmic complexity O(N) and success rate give by coefficient THID:EER=0.38% and GPDS:EER=0.35% on real images.

ECG-based biometric identification: some modern approaches

2016 ◽  
pp. 35-39
Author(s):  
A.A. Astapov ◽  
◽  
D.V. Davydov ◽  
A.I. Egorov ◽  
D.V. Drozdov ◽  
...  
Keyword(s):  
Biometric Identification

scholarly journals MultiStep Ahead Forecasting for Hourly PM10 and PM2.5 Based on Two-Stage Decomposition Embedded Sample Entropy and Group Teacher Optimization Algorithm

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 64
Author(s):  
Feng Jiang ◽  
Yaqian Qiao ◽  
Xuchu Jiang ◽  
Tianhai Tian
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Approach ◽  
Forecast Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Air Pollutant ◽  
Intrinsic Mode Functions ◽  
Concentration Data ◽  
Two Stage ◽  
Pm10 And Pm2.5

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data.

scholarly journals EEG microstate in obstructive sleep apnea patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Xiong ◽  
Yuyan Ren ◽  
Shenghan Gao ◽  
Jianhua Luo ◽  
Jiangli Liao ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Correlation Analysis ◽  
Transition Probability ◽  
Transient State ◽  
Sample Entropy ◽  
Sleep Stages ◽  
Fluctuation Analysis ◽  
Alpha Power ◽  
Obstructive Sleep

AbstractObstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

scholarly journals A Novel S-Box Dynamic Design Based on Nonlinear-Transform of 1D Chaotic Maps

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1313
Author(s):  
Wenhao Yan ◽  
Qun Ding
Keyword(s):  
Chaotic Systems ◽  
Chaotic Maps ◽  
Chaotic Map ◽  
Logistic Map ◽  
Sample Entropy ◽  
One Dimension ◽  
Linear Combinations ◽  
Dynamic Design ◽  
Construction Methods ◽  
Low Dimensional

In this paper, a method to enhance the dynamic characteristics of one-dimension (1D) chaotic maps is first presented. Linear combinations and nonlinear transform based on existing chaotic systems (LNECS) are introduced. Then, a numerical chaotic map (LCLS), based on Logistic map and Sine map, is given. Through the analysis of a bifurcation diagram, Lyapunov exponent (LE), and Sample entropy (SE), we can see that CLS has overcome the shortcomings of a low-dimensional chaotic system and can be used in the field of cryptology. In addition, the construction of eight functions is designed to obtain an S-box. Finally, five security criteria of the S-box are shown, which indicate the S-box based on the proposed in this paper has strong encryption characteristics. The research of this paper is helpful for the development of cryptography study such as dynamic construction methods based on chaotic systems.

Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings

2021 ◽  
Author(s):  
Beatriz García-Martínez ◽  
Antonio Fernández-Caballero ◽  
Raúl Alcaraz ◽  
Arturo Martínez-Rodrigo
Keyword(s):  
Brain Activity ◽  
Sample Entropy
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