This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural...This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network （ANN） and support vector machines （SVMs） combined with su- pervised learning algorithms, and k-means clustering （k-MC） combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One （OvO）, One Vs All （OVA） and Binary Decision Tree （BDT）, are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour （k- NN） and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise.展开更多
Objective As the core unit of the limbic system,the hippocampus is involved in the regulation of higher neural activity by integrating emotional encoding and memory storage functions.In the pathological process of epi...Objective As the core unit of the limbic system,the hippocampus is involved in the regulation of higher neural activity by integrating emotional encoding and memory storage functions.In the pathological process of epilepsy,structural remodeling and functional disorders in this region have become the focus of research,and the existing evidence mostly focuses on hippocampal sclerosis,a typical neurodegenerative change.However,there is still a lack of systematic analysis of the pathological subtypes under the International League Against Epilepsy(ILAE)classification system in cross-scale molecular events such as epigenetic regulation and microbiome-brain axis.By integrating clinical cohort data and experimental model evidence,this article focuses on the association characteristics between hippocampal sclerosis subtypes and seizure patterns,and reveals the formation of abnormal hippocampal network and the cascading effect of abnormal hippocampus-related neurotransmitters in the formation of epileptogenic network.The study found that specific pathological subtypes showed a significant correspondence with seizure frequency and drug sensitivity,suggesting that hippocampal sclerosis drives epilepsy progression through multidimensional molecular events.In the future,it is necessary to combine spatial transcriptome and targeted metabolomics technology to analyze the cell interaction network in the hippocampal microenvironment,so as to provide a theoretical basis for the development of subtype-specific antiepileptic strategies.展开更多
文摘This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network （ANN） and support vector machines （SVMs） combined with su- pervised learning algorithms, and k-means clustering （k-MC） combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One （OvO）, One Vs All （OVA） and Binary Decision Tree （BDT）, are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour （k- NN） and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise.
文摘Objective As the core unit of the limbic system,the hippocampus is involved in the regulation of higher neural activity by integrating emotional encoding and memory storage functions.In the pathological process of epilepsy,structural remodeling and functional disorders in this region have become the focus of research,and the existing evidence mostly focuses on hippocampal sclerosis,a typical neurodegenerative change.However,there is still a lack of systematic analysis of the pathological subtypes under the International League Against Epilepsy(ILAE)classification system in cross-scale molecular events such as epigenetic regulation and microbiome-brain axis.By integrating clinical cohort data and experimental model evidence,this article focuses on the association characteristics between hippocampal sclerosis subtypes and seizure patterns,and reveals the formation of abnormal hippocampal network and the cascading effect of abnormal hippocampus-related neurotransmitters in the formation of epileptogenic network.The study found that specific pathological subtypes showed a significant correspondence with seizure frequency and drug sensitivity,suggesting that hippocampal sclerosis drives epilepsy progression through multidimensional molecular events.In the future,it is necessary to combine spatial transcriptome and targeted metabolomics technology to analyze the cell interaction network in the hippocampal microenvironment,so as to provide a theoretical basis for the development of subtype-specific antiepileptic strategies.
