scholarly journals Chaotic Analysis of the Electroretinographic Signal for Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Surya S. Nair ◽  
K. Paul Joseph
Keyword(s):  
Approximate Entropy ◽  
Physiological Signals ◽  
Largest Lyapunov Exponent ◽  
Time Varying ◽  
Congenital Stationary Night Blindness ◽  
Recurrence Plots ◽  
Control Groups ◽  
Nonlinear Parameters ◽  
Naked Eye ◽  
Signal Features

Electroretinogram (ERG) is a time-varying potential which arises from different layers of retina. To be specific, all the physiological signals may contain some useful information which is not visible to our naked eye. However this subtle information is difficult to monitor directly. Therefore the ERG signal features which are extracted and analyzed using computers are highly useful for diagnosis. This work discusses the chaotic aspect of the ERG signal for the controls, congenital stationary night blindness (CSNB), and cone-rod dystrophy (CRD) classes. In this work, nonlinear parameters like Hurst exponent (HE), the largest Lyapunov exponent (LLE), Higuchi’s fractal dimension (HFD), and approximate entropy (ApEn) are analyzed for the three different classes. It is found that the measures like HE dimension and ApEn are higher for controls as compared to the other two classes. But LLE shows no distinguishable variation for the three cases. We have also analyzed the recurrence plots and phase-space plots which shows a drastic variation among the three groups. The results obtained show that the ERG signal is highly complex for the control groups and less complex for the abnormal classes withPvalue less than 0.05.

scholarly journals Multiscale Cross-Approximate Entropy Analysis as a Measure of Complexity among the Aged and Diabetic

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hsien-Tsai Wu ◽  
Cyuan-Cin Liu ◽  
Men-Tzung Lo ◽  
Po-Chun Hsu ◽  
An-Bang Liu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Time Series ◽  
Pulse Transit Time ◽  
Approximate Entropy ◽  
Physiological Signals ◽  
Diabetes Group ◽  
Group 4 ◽  
Group 2 ◽  
Aged Subjects

Complex fluctuations within physiological signals can be used to evaluate the health of the human body. This study recruited four groups of subjects: young healthy subjects (Group 1,n=32), healthy upper middle-aged subjects (Group 2,n=36), subjects with well-controlled type 2 diabetes (Group 3,n=31), and subjects with poorly controlled type 2 diabetes (Group 4,n=24). Data acquisition for each participant lasted 30 minutes. We obtained data related to consecutive time series with R-R interval (RRI) and pulse transit time (PTT). Using multiscale cross-approximate entropy (MCE), we quantified the complexity between the two series and thereby differentiated the influence of age and diabetes on the complexity of physiological signals. This study used MCE in the quantification of complexity between RRI and PTT time series. We observed changes in the influences of age and disease on the coupling effects between the heart and blood vessels in the cardiovascular system, which reduced the complexity between RRI and PTT series.

A COMPUTATIONAL FRAMEWORK TO DISCRIMINATE DIFFERENT ANESTHESIA STATES FROM EEG SIGNAL

2018 ◽  
Vol 30 (03) ◽  
pp. 1850020 ◽  
Author(s):  
Seyyed Abed Hosseini
Keyword(s):  
General Anesthesia ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Approximate Entropy ◽  
Largest Lyapunov Exponent ◽  
Eeg Signal ◽  
Wavelet Coefficients ◽  
Sparse Principal Component Analysis ◽  
Beta Band ◽  
Computational Framework

This paper develops a computational framework to classify different anesthesia states, including awake, moderate anesthesia, and general anesthesia, using electroencephalography (EEG) signal. The proposed framework presents data gathering; preprocessing; appropriate selection of window length by genetic algorithm (GA); feature extraction by approximate entropy (ApEn), Petrosian fractal dimension (PFD), Hurst exponent (HE), largest Lyapunov exponent (LLE), Lempel-Ziv complexity (LZC), correlation dimension (CD), and Daubechies wavelet coefficients; feature normalization; feature selection by non-negative sparse principal component analysis (NSPCA); and classification by radial basis function (RBF) neural network. Because of the small number of samples, a five-fold cross-validation approach is used to validate the results. A GA is used to select that by observing an interval of 2.7[Formula: see text]s for further assessment. This paper assessed superior features, such as LZC, ApEn, PFD, HE, the mean value of wavelet coefficients for the beta band, and LLE. The results indicate that the proposed framework can classify different anesthesia states, including awake, moderate anesthesia, and general anesthesia, with an accuracy of 92.07%, 96.18%, and 93.42%, respectively. Therefore, the proposed framework can discriminate different anesthesia states with an average accuracy of 93.89%. Finally, the proposed framework provided a facilitative representation of the brain’s behavior in different states of anesthesia.

Cognitive Analytics for Rapid Stress Relief in Humans Using EEG Based Analysis of Tratak Sadhana (Meditation)

2020 ◽  
Vol 10 (4) ◽  
pp. 1-20
Author(s):  
Swati Kamthekar ◽  
Prachi Deshpande ◽  
Brijesh Iyer
Keyword(s):  
Big Data ◽  
Fractal Dimensions ◽  
Approximate Entropy ◽  
Stress Relief ◽  
Eeg Signals ◽  
Nonlinear Parameters ◽  
Wavelet Entropy ◽  
Rest State ◽  
Eyes Closed ◽  
The Brain

The article reports the effect of Tratak Sadhana (meditation) on humans using electroencephalograph (EEG) signals. EEG represents the brain activities in the form of electrical signals. Due to non-stationary nature of the EEG signals, nonlinear parameters like approximate entropy, wavelet entropy and Higuchi' fractal dimensions are used to assess the variations in EEG rest as well as during Tratak Sadhana, i.e. at a rest state with eyes closed and during Tratak meditation. EEG signals are captured using EPOC Emotive EEG sensor. The sensor has 14 electrodes covering human scalp. Results shows that new practitioners can also achieve a rapid meditative state as compared to other meditation techniques. Further, the Big Data perspective of the present study is discussed. The present study shows that Tratak Sadhana meditation is an effective tool for rapid stress relief in humans.

Research of Extracting Information about Reservoir Logging Signals Based on Chaos Characteristics

2013 ◽  
Vol 380-384 ◽  
pp. 3742-3745
Author(s):  
Chun Yan Nie ◽  
Rui Li ◽  
Wan Li Zhang
Keyword(s):  
Lyapunov Exponent ◽  
Correlation Dimension ◽  
Approximate Entropy ◽  
Water Layer ◽  
Time Correlation ◽  
Largest Lyapunov Exponent ◽  
Characteristic Parameters ◽  
Chaotic Characteristic ◽  
Oil Water

The mechanism of logging signals generating was researched. In the same time, correlation dimension, largest Lyapunov exponent and approximate entropy of chaotic characteristics were extracted. On this basis, chaotic characteristic parameters were applied in processing, analysis and interpretation, try to find chaotic characteristics of different of reservoirs for example oil, water layer and the dry layer. The results showed that chaos characteristics in different reservoir is different, therefore, we can distinguish the different natures of reservoirs by extracting chaos characteristics.

scholarly journals Application of Time-Scale Decomposition of Entropy for Eye Movement Analysis

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 168 ◽  
Author(s):  
Katarzyna Harezlak ◽  
Pawel Kasprowski
Keyword(s):  
Eye Movement ◽  
Time Scale ◽  
Movement Analysis ◽  
Approximate Entropy ◽  
Largest Lyapunov Exponent ◽  
Eye Movement Analysis ◽  
Movement Dynamics ◽  
Signal Characteristics ◽  
Time Scale Decomposition ◽  
Events Detection

The methods for nonlinear time series analysis were used in the presented research to reveal eye movement signal characteristics. Three measures were used: approximate entropy, fuzzy entropy, and the Largest Lyapunov Exponent, for which the multilevel maps (MMs), being their time-scale decomposition, were defined. To check whether the estimated characteristics might be useful in eye movement events detection, these structures were applied in the classification process conducted with the usage of the kNN method. The elements of three MMs were used to define feature vectors for this process. They consisted of differently combined MM segments, belonging either to one or several selected levels, as well as included values either of one or all the analysed measures. Such a classification produced an improvement in the accuracy for saccadic latency and saccade, when compared with the previously conducted studies using eye movement dynamics.

scholarly journals Dynamical Analysis of a New Chaotic Fractional Discrete-Time System and Its Control

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1344
Author(s):  
A. Othman Almatroud ◽  
Amina-Aicha Khennaoui ◽  
Adel Ouannas ◽  
Giuseppe Grassi ◽  
M. Mossa Al-sawalha ◽  
...  
Keyword(s):  
Discrete Time ◽  
Software Package ◽  
Control Method ◽  
Approximate Entropy ◽  
Dynamical Analysis ◽  
Largest Lyapunov Exponent ◽  
Bifurcation Diagrams ◽  
Discrete Time System ◽  
Time System ◽  
Quadratic Nonlinearities

This article proposes a new fractional-order discrete-time chaotic system, without equilibria, included two quadratic nonlinearities terms. The dynamics of this system were experimentally investigated via bifurcation diagrams and largest Lyapunov exponent. Besides, some chaotic tests such as the 0–1 test and approximate entropy (ApEn) were included to detect the performance of our numerical results. Furthermore, a valid control method of stabilization is introduced to regulate the proposed system in such a way as to force all its states to adaptively tend toward the equilibrium point at zero. All theoretical findings in this work have been verified numerically using MATLAB software package.

NONLINEAR ANALYSIS OF PHYSIOLOGICAL SIGNALS: A REVIEW

2012 ◽  
Vol 12 (04) ◽  
pp. 1240015 ◽  
Author(s):  
OLIVER FAUST ◽  
MURALIDHAR G. BAIRY
Keyword(s):  
Nonlinear Analysis ◽  
Biomedical Engineering ◽  
Scientific Literature ◽  
Approximate Entropy ◽  
Extraction Methods ◽  
Conclusive Evidence ◽  
Physiological Signals ◽  
Spectral Entropy ◽  
Nonlinear Features ◽  
Renyi’S Entropy

This paper reviews various nonlinear analysis methods for physiological signals. The assessment is based on a discussion of chaos-inspired methods, such as fractal dimension (FD), correlation dimension (D2), largest Lyapunov exponet (LLE), Renyi's entropy (REN), Shannon spectral entropy (SEN), and approximate entropy (ApEn). We document that these methods are used to extract discriminative features from electroencephalograph (EEG) and heart rate variability (HRV) signals by reviewing the relevant scientific literature. EEG features can be used to support the diagnosis of epilepsy and HRV features can be used to support the diagnosis of cardiovascular diseases as well as diabetes. Documenting the widespread use of these and other nonlinear methods supports our thesis that the study of feature extraction methods, based on the chaos theory, is an important subject which has been gaining more and significance in biomedical engineering. We adopt the position that pursuing research in the field of biomedical engineering is ultimately a progmatic activity, where it is necessary to engage in features that work. In this case, the nonlinear features are working well, even if we do not have conclusive evidence that the underlying physiological phenomena are indeed chaotic.

scholarly journals Dynamics Analysis and Fractional-Order Approximate Entropy of Nonlinear Inventory Management Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tengfei Lei ◽  
Rita Yi Man Li ◽  
Haiyan Fu
Keyword(s):  
Lyapunov Exponent ◽  
Inventory Management ◽  
Approximate Entropy ◽  
Human Action ◽  
Management Model ◽  
Management Systems ◽  
Largest Lyapunov Exponent ◽  
Complex Nonlinear Systems ◽  
Definition Of ◽  
Special Circumstances

Inventory management is complex nonlinear systems that are affected by various external factors, including course human action and policy. We study the inventory management model under special circumstances and analyse the equilibrium point of the system. The dynamics of the system is analysed by means of the eigenvalue trajectory, bifurcations, chaotic attractor, and largest Lyapunov exponent diagram. At the same time, according to the definition of fractional calculus, the fractional approximate entropy is used to analyse the system, and the results are consistent with those of the largest Lyapunov exponent diagram, which shows the effectiveness of this method.

EFFECTS OF MOBILE PHONE RADIATION ON CARDIAC HEALTH

2011 ◽  
Vol 11 (05) ◽  
pp. 1241-1253 ◽  
Author(s):  
OLIVER FAUST ◽  
U. RAJENDRA ACHARYA ◽  
MYAGMARBAYAR NERGUI ◽  
DHANJOO N GHISTA ◽  
SUBHAGATA CHATTOPADHYAY ◽  
...  
Keyword(s):  
Health Risks ◽  
Radiation Effects ◽  
Approximate Entropy ◽  
Largest Lyapunov Exponent ◽  
Pathological Effect ◽  
Neurological Effects ◽  
The Masses ◽  
Result Analysis ◽  
Parameter Values ◽  
Mobile Phone Radiation

Mobile phones (MPs) progressed from a tool of the privileged few to a gadget for the masses. However, the physical effects, which enable wireless information transmission, did not change; MP technology still relies on pulsed high-frequency electromagnetic (EM) fields. Therefore, the health risks, associated with EM fields, remain. Studies that investigated these health risks have reported dizziness, numbness in the thigh, and heaviness in the chest. This study investigates neurological effects that are caused by EM fields radiated from MPs. The heart rate variability (HRV) can be used as a measure for these neurological effects, because the automated nervous system modulates the HRV. We measured the HRV of 14 healthy male volunteers. We used the following nonlinear parameters to quantify the MP radiation effects on HRV: approximate entropy (ApEn), capacity dimension (CaD), correlation dimension (CD), fractal dimension (FD), Hurst exponent (H), and the largest Lyapunov exponent (LLE). The results indicate that there is a measurable difference in the parameter values when the MP is kept close to the chest and when it is kept close to the head. However, these differences are very small and statistical analysis showed that they have no clinical significance. Furthermore, the result analysis does not show a consistent trend, which indicates that there is no underlying pathological effect.

Characterization of Heart Rate Variability Changes Following Asphyxia in Rats

2004 ◽  
Vol 43 (01) ◽  
pp. 118-121 ◽  
Author(s):  
L. Moraru ◽  
L. Cimponeriu ◽  
S. Tong ◽  
N. Thakor ◽  
A. Bezerianos
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Cardiac Arrest ◽  
Frequency Domain ◽  
Linear Time ◽  
Approximate Entropy ◽  
Recovery Process ◽  
Autonomous Nervous System ◽  
Nonlinear Parameters

Summary Objectives: A non-invasive method to monitor the functioning of the autonomous nervous system consists in heart rate variability (HRV) analysis. The aim of this study was to investigate the changes on HRV after an asphyxia experiment in rats, using several linear (time and frequency domain) and nonlinear parameters (approximate entropy, SD1 and SD2 indices derived from Poincare plots). Methods: The experiments involved the study of HRV changes after cardiac arrest (CA) resulting from 5 min of hypoxia and asphyxia, followed by manual resuscitation and return of spontaneous circulation. 5 min stationary periods of RR intervals were selected for further analysis from 5 rats in following distinct situations: 1) baseline, 2) 30 min after CA, 3) 60 min after CA, 4) 90 min after CA, 5) 120 min after CA, 6) 150 min after CA. The ANS contribution has been delineated based on time and frequency domain analysis. Results and Conclusions: The results indicate that the recovery process following the asphyxia cardiac arrest reflects the impaired functioning of the autonomic nervous system. Both linear and nonlinear parameters track the different phases of the experiment, with an increased sensitivity displayed by the approximate entropy (ApEn). After 150 min the ApEn RRI parameter recovers to its baseline value. The results forward the ApEn as a more sensitive parameter of the recovery process following the asphyxia.

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