Decomposing Spectral and Phasic Differences in Nonlinear Features between Datasets

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

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Nonlinear Features of Fano Resonance: A QM/EM Study

Physical Chemistry Chemical Physics ◽

10.1039/d1cp02459k ◽

2021 ◽

Author(s):

Jin Sun ◽

Zongling Ding ◽

Yuanqin Yu ◽

WanZhen Liang

Keyword(s):

Hybrid Systems ◽

Fano Resonance ◽

Hybrid Approach ◽

Nonlinear Features ◽

Fano Effects ◽

Dye Molecule

The nonlinear Fano effects on the absorption of hybrid systems composed of a silver nanosphere and an indoline dye molecule have been systematically investigated by the hybrid approach, which combines...

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Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model

Applied Sciences ◽

10.3390/app11020787 ◽

2021 ◽

Vol 11 (2) ◽

pp. 787

Author(s):

Bartłomiej Ambrożkiewicz ◽

Grzegorz Litak ◽

Anthimos Georgiadis ◽

Nicolas Meier ◽

Alexander Gassner

Keyword(s):

Mathematical Model ◽

Degrees Of Freedom ◽

Ball Bearing ◽

Hertzian Contact ◽

Two Degrees Of Freedom ◽

Wide Range ◽

Shape Errors ◽

Nonlinear Features ◽

Fourier Transform Phase ◽

Hertzian Contact Theory

Often the input values used in mathematical models for rolling bearings are in a wide range, i.e., very small values of deformation and damping are confronted with big values of stiffness in the governing equations, which leads to miscalculations. This paper presents a two degrees of freedom (2-DOF) dimensionless mathematical model for ball bearings describing a procedure, which helps to scale the problem and reveal the relationships between dimensionless terms and their influence on the system’s response. The derived mathematical model considers nonlinear features as stiffness, damping, and radial internal clearance referring to the Hertzian contact theory. Further, important features are also taken into account including an external load, the eccentricity of the shaft-bearing system, and shape errors on the raceway investigating variable dynamics of the ball bearing. Analysis of obtained responses with Fast Fourier Transform, phase plots, orbit plots, and recurrences provide a rich source of information about the dynamics of the system and it helped to find the transition between the periodic and chaotic response and how it affects the topology of RPs and recurrence quantificators.

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Subbands and cumulative sum of subbands based nonlinear features enhance the performance of epileptic seizure detection

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102827 ◽

2021 ◽

Vol 69 ◽

pp. 102827

Author(s):

Tao Zhang ◽

Zhiwu Han ◽

Xiaojuan Chen ◽

Wanzhong Chen

Keyword(s):

Epileptic Seizure ◽

Seizure Detection ◽

Cumulative Sum ◽

Epileptic Seizure Detection ◽

Nonlinear Features

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COMPARISON OF ICTAL AND INTERICTAL EEG SIGNALS USING FRACTAL FEATURES

International Journal of Neural Systems ◽

10.1142/s0129065713500287 ◽

2013 ◽

Vol 23 (06) ◽

pp. 1350028 ◽

Cited By ~ 34

Author(s):

YU WANG ◽

WEIDONG ZHOU ◽

QI YUAN ◽

XUELI LI ◽

QINGFANG MENG ◽

...

Keyword(s):

Feature Analysis ◽

Support Vector ◽

Eeg Analysis ◽

Eeg Signals ◽

Eeg Classification ◽

Significant Difference ◽

Interictal Eeg ◽

Nonlinear Features ◽

The Difference ◽

Multi Scales

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

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An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features

Knowledge-Based Systems ◽

10.1016/j.knosys.2015.03.015 ◽

2015 ◽

Vol 83 ◽

pp. 149-158 ◽

Cited By ~ 69

Author(s):

U. Rajendra Acharya ◽

Hamido Fujita ◽

Vidya K. Sudarshan ◽

Vinitha S. Sree ◽

Lim Wei Jie Eugene ◽

...

Keyword(s):

Wavelet Transform ◽

Sudden Cardiac Death ◽

Discrete Wavelet Transform ◽

Cardiac Death ◽

Discrete Wavelet ◽

Integrated Index ◽

Nonlinear Features

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Extracting nonlinear features for multispectral images by FCMC and KPCA

Digital Signal Processing ◽

10.1016/j.dsp.2004.12.004 ◽

2005 ◽

Vol 15 (4) ◽

pp. 331-346 ◽

Cited By ~ 36

Author(s):

Zhan-Li Sun ◽

De-Shuang Huang ◽

Yiu-Ming Cheun

Keyword(s):

Multispectral Images ◽

Nonlinear Features

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Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

Journal of Applied Physiology ◽

10.1152/japplphysiol.00059.2017 ◽

2017 ◽

Vol 123 (2) ◽

pp. 344-351 ◽

Cited By ~ 14

Author(s):

Luiz Eduardo Virgilio Silva ◽

Renata Maria Lataro ◽

Jaci Airton Castania ◽

Carlos Alberto Aguiar Silva ◽

Helio Cesar Salgado ◽

...

Keyword(s):

Nonlinear Dynamics ◽

Heart Rate ◽

Heart Rate Variability ◽

Time Scales ◽

Multiscale Entropy ◽

Time Frequency ◽

Cardiac Control ◽

Long Time ◽

Nonlinear Features ◽

Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

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Master-Slave Synchronization of 4D Hyperchaotic Rabinovich Systems

Complexity ◽

10.1155/2018/6520474 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Ke Ding ◽

Christos Volos ◽

Xing Xu ◽

Bin Du

Keyword(s):

Control Methods ◽

Nonlinear Features ◽

Local Synchronization ◽

And Control

This paper is concerned with master-slave synchronization of 4D hyperchaotic Rabinovich systems. Compared with some existing papers, this paper has two contributions. The first contribution is that the nonlinear terms of error systems remained which inherit nonlinear features from master and slave 4D hyperchaotic Rabinovich systems, rather than discarding nonlinear features of original hyperchaotic Rabinovich systems and eliminating those nonlinear terms to derive linear error systems as the control methods in some existing papers. The second contribution is that the synchronization criteria of this paper are global rather than local synchronization results in some existing papers. In addition, those synchronization criteria and control methods for 4D hyperchaotic Rabinovich systems are extended to investigate the synchronization of 3D chaotic Rabinovich systems. The effectiveness of synchronization criteria is illustrated by three simulation examples.

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Diffraction Waves About an Advancing Wedge Model in Deep Water

Journal of Ship Research ◽

10.5957/jsr.1990.34.2.105 ◽

1990 ◽

Vol 34 (02) ◽

pp. 105-122

Author(s):

Hideaki Miyata ◽

Makoto Kanai ◽

Noriaki Yoshiyasu ◽

Yohichi Furuno

Keyword(s):

Numerical Simulation ◽

Finite Difference ◽

Finite Difference Method ◽

Wedge Angle ◽

Wave Pattern ◽

Regular Waves ◽

Nonlinear Characteristics ◽

Diffracted Waves ◽

Nonlinear Features ◽

Incident Waves

The diffraction of regular waves by advancing wedge models is studied both experimentally and numerically. The nonlinear features of diffracted waves are visualized by wave pattern pictures and the formation is analyzed by the grid-projection method. The experimental observation indicates that the diffracted waves have a number of nonlinear characteristics similar to shock waves due to the interaction of incident waves with the advancing obstacle in the flow-field caused by the advancing motion. Bow waves of both oblique type and normal detached type are observed at remarkably lower Froude numbers than in the case of a ship in steady advance motion. Their occurrence systematically depends on the Froude number and the wedge angle. The numerical simulation of this phenomenon by a finite-difference method shows approximate agreement with the experimental results.

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