JIAO HU, JIALU MO, XIANGLIN CHENG^*

BIOCELL, Vol.47, No.8, pp. 1689-1701, 2023, DOI:10.32604/biocell.2023.029409

Abstract Epilepsy is one of the most common neurological diseases worldwide with a high prevalence and unknown pathogenesis. Further, its control is challenging. It is generally accepted that an imbalance between the excitatory and inhibitory properties of the central nervous system (CNS) leads to a large number of abnormally synchronized neuronal discharges in the brain. Transient receptor potential vanilloid protein type 1 (TRPV1) is a non-selective cation channel that contributes to the regulation of the nervous system and influences the excitability of the nervous system. This includes the release of neurotransmitters, action potential generation due to alterations in ion channels, synaptic… More >

Zaihe Cheng¹, Yuwen Tao², Xiaoqing Gu³, Yizhang Jiang², Pengjiang Qian^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1613-1633, 2023, DOI:10.32604/cmes.2023.027708

Abstract Through semi-supervised learning and knowledge inheritance, a novel Takagi-Sugeno-Kang (TSK) fuzzy system framework is proposed for epilepsy data classification in this study. The new method is based on the maximum mean discrepancy (MMD) method and TSK fuzzy system, as a basic model for the classification of epilepsy data. First, for medical data, the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe. Second, in view of the deviation in the data distribution between the real source domain and the target domain, MMD is used to measure the distance between dierent data distributions. The objective… More >

Jiali Wang¹, Bing Li², Chengyu Qiu¹, Xinyun Zhang¹, Yuting Cheng¹, Peihua Wang¹, Ta Zhou³, Hong Ge², Yuanpeng Zhang^1,3,*, Jing Cai^3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4843-4866, 2023, DOI:10.32604/cmc.2023.037457

Abstract Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source… More >

Farah Mohammad^*, Saad Al-Ahmadi

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 623-639, 2023, DOI:10.32604/cmc.2023.032552

Abstract

There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models… More >

Jian Qiang Wang¹, Wei Fang^1,2,*, Victor S. Sheng³

Journal of New Media, Vol.4, No.2, pp. 73-84, 2022, DOI:10.32604/jnm.2022.027040

Abstract Brain-Computer Interface (BCI) technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life. People use this technology to capture brain waves and analyze the electroencephalograph (EEG) signal for feature extraction. Take the medical field as an example, epilepsy disease is threatening human health every moment. We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases, overcoming the problem that the disease requires long time… More >

S. Beatrice^*, Janaki Meena

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 331-347, 2022, DOI:10.32604/iasc.2022.023666

Abstract Discovering the information about several disorders prevailing in brain and neurology is by no means a new scientific technique. A neurological disorder of any human being can be analyzed using EEG (Electroencephalography) signal from the electrode’s output. Epilepsy (spontaneous recurrent seizure) detection is usually carried out by the physicians using a visual scanning of the signals produced by EEG, which is onerous and may be inaccurate. EEG signal is often used to determine epilepsy, for its merits, such as non-invasive, portable, and economical, can exhibit superior temporal tenacity. This paper surveys the existing artifact removal methods. It puts a new-fangled… More >

Sengul Bayrak^1,2, Eylem Yucel^2,*, Hidayet Takci³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3589-3607, 2022, DOI:10.32604/cmc.2022.018742

Abstract The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given… More >

Sidra Naseem¹, Kashif Javed¹, Muhammad Jawad Khan¹, Saddaf Rubab², Muhammad Attique Khan³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 471-486, 2021, DOI:10.32604/cmc.2021.018239

Abstract Electroencephalography is a common clinical procedure to record brain signals generated by human activity. EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications, but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience. Various EEG analysis and classification techniques have been proposed to address this problem however, the conventional classification methods require identification and learning of specific EEG characteristics beforehand. Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification. One of the great implementations of deep learning is Convolutional Neural Network… More >

Shamsah Majed Alotaibi¹, Atta-ur-Rahman¹, Mohammed Imran Basheer¹, Muhammad Adnan Khan^2,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 149-165, 2021, DOI:10.32604/cmc.2021.015976

Abstract Epilepsy is a type of brain disorder that causes recurrent seizures. It is the second most common neurological disease after Alzheimer’s. The effects of epilepsy in children are serious, since it causes a slower growth rate and a failure to develop certain skills. In the medical field, specialists record brain activity using an Electroencephalogram (EEG) to observe the epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate due to human errors; therefore, automated detection of epileptic pediatric seizures might be the optimal solution. This paper investigates the detection of epileptic seizures… More >

D. Venugopal¹, T. Jayasankar^2,*, N. Krishnaraj³, S. Venkatraman⁴, N. B. Prakash⁵, G. R. Hemalakshmi⁵

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 27-38, 2021, DOI:10.32604/iasc.2021.015205

Abstract Comprehensive evaluation of common complex diseases associated with common gene mutations is currently a hot area of human genome research into causative new developments. A multi-fractal analysis of the genome is performed by placing the entire DNA sequence into smaller fragments and using the chaotic game representation and systematic methods to calculate the general dimensional spectrum of each fragment. This is a time consuming process as it uses floating point to represent large data sets and requires processing time. The proposed Regressive Multi-Array Deep Neural Classifier (RMDNC) system is implemented to reduce the computation time, it is called a polymorphic… More >