A new chaotic network model for epilepsy

Panahi, Shirin; Shirzadian, Touraj; Jalili, Mahdi; Jafari, Sajad

doi:10.1016/j.amc.2018.10.061

Cited by 12 publications

(7 citation statements)

References 42 publications

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“…The controller output signals u 1 , u 3 , where u 1 has been used to control the first state and u 3 has been used to control the second state. They both have five intuitionistic fuzzy sets; the centers of those control signals have to be updated according to (14). The initial values of the centers for both control signals are the same, where u i1 −3, u i2 −1.5, u i3 0, u i4 1.5, u i5 3, where i is the control signal number.…”

Section: Experimental Examplementioning

confidence: 99%

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Control in the loop for synchronization of nonlinear chaotic systems via adaptive intuitionistic neuro-fuzzy: a comparative study

Helmy

Magdy

Hamdy

2022

Complex Intell. Syst.

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This paper proposes control in the loop (CIL) for the synchronization between two nonlinear chaotic systems at the existence of uncertainties and disturbances using an adaptive intuitionistic neuro-fuzzy (AINF) control scheme. The chaotic systems have been subedited as one is the master and the other is the slave. They both have different initial conditions and parameters. The variation in the initial conditions leads to the butterfly effect, the concept that is well known in the chaos field and means that both systems diverge over time. Therefore, AINF control scheme has been proposed in this paper as a powerful scheme to get over this problem effectively. The main objective of using the AINF control scheme is that it collects the features of its contents. As the intuitionistic fuzzy gives the system flexibility and helps the controller collecting more information about the problem. The neural networks give the controller the ability to learn over time. The experimental results were obtained to verify the applicability and effectiveness of the proposed control scheme against external disturbance and model uncertainties with comparative study.

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Section: Experimental Examplementioning

confidence: 99%

“…Chaos synchronization has an important role in model calibration [11] and secure communications [12]. Synchronization is important in many medical applications like some brain pathologies as Parkinson's disease [13], epilepsy [14], schizophrenia [15], and Alzheimer's disease [16]. It has a part in natural rhythms as heart beating [17].…”

Section: Introductionmentioning

confidence: 99%

Control in the loop for synchronization of nonlinear chaotic systems via adaptive intuitionistic neuro-fuzzy: a comparative study

Helmy

Magdy

Hamdy

2022

Complex Intell. Syst.

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“…The regularization parameter λ ∈ 10 −4 , 10 −3 , 10 −2 , 10 −1 , 10 0 , 10, 10 2 È É SVM The penalty factor c ∈ 2 -5 , 2 -4 , 2 -3 ,⋯,2 3 , 2 4 , 2 5 È É , the kernel parameter δ ∈ 2 -5 , 2 -4 , 2 -3 ,⋯,2 3 , 2 4 , 2 5 È É…”

Section: Dlsrunclassified

“…The Lagrange multiplier upper bound c ∈ 2 -5 , 2 -4 , 2 -3 ,⋯,2 3 , 2 4 , 2 5 È É , the kernel parameter δ ∈ 2 -5 , 2 -4 , 2 -3 ,⋯,2 3 , 2 4 , 2 5 È É…”

Section: Tsvmunclassified

“…To predict the onset or preonset of epilepsy in the process of seizure detection, machine learning and pattern classification algorithms are generally applied to classify the EEG signals after extracting the characteristics of the time domain, frequency domain, time frequency domain, or nonlinear domain of the EEG. With the development of computer technology and digital signal processing technology, more and more methods are widely used in the study of seizure detection methods and have achieved certain research results, such as the Bayesian classifier [ 4 ], artificial neural network [ 5 – 9 ], support vector machine (SVM) [ 10 – 13 ], and fuzzy reasoning [ 14 , 15 ]. For example, Obeyli extracted the Lyapunov exponential features of EEG signals and used probabilistic neural networks to classify EEG signals, so as to achieve high classification results [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning

Yao

Cui

2020

Computational and Mathematical Methods in Medicine

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Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients’ health, cognition, etc. In the current condition, EEG plays a vital role in the diagnosis, judgment, and qualitative location of epilepsy among the clinical diagnosis of various epileptic seizures and is an indispensable means of detection. The study of the EEG signals of patients with epilepsy can provide a strong basis and useful information for in-depth understanding of its pathogenesis. Although, intelligent classification technologies based on machine learning have been widely used to the classification of epilepsy EEG signals and show the effectiveness. In fact, it is difficult to ensure that there is always enough EEG data available for training the model in real life, which will affect the performance of the algorithms. In view of this, to reduce the impact of insufficient data on the detection performance of the algorithms, a novel discriminate least squares regression- (DLSR-) based inductive transfer learning method was introduced which is on the basis of DLSR and the inductive transfer learning. And, it is applied to promote the adaptability and accuracy of the epilepsy EEG signal recognition. The proposed method inherits the advantages of DLSR; it can be more suitable for classification scenarios by expanding the interval between different classes. Meanwhile, it can simultaneously use the data of the target domain and the knowledge of the source domain, which is helpful for getting better performance. The results show that the improved method has more advantages in EEG signal recognition comparing to several other representative methods.

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