儿童癫痫综合征智能分析:综述与展望

doi:10.16451/j.cnki.issn1003-6059.202402004

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Abstract

The intelligent analysis of childhood epileptic syndrome refers to the research which aims at addressing clinical and prognostic management issues by data-driven methods such as statistical analysis and machine learning to explore clinically effective biomarkers and construct corresponding expert systems. Firstly, the definition, seizure types and classification of childhood epileptic syndrome are briefly introduced. Then, the advantages and disadvantages of the framework and typical methods of the intelligent analysis of childhood epileptic syndrome based on scalp electroencephalogram are reviewed, including data collection and preprocessing, feature extraction, decision-making systems, and expert systems. Specifically, the expert systems are divided into specific waveform detection systems, diagnostic classification systems, seizure detection systems, seizure prediction systems and quantitative assessment systems with a comprehensive summary and theoretical explanation. Finally, with the consideration of the limitations and challenges of the existing research in the field of intelligent analysis of childhood epileptic syndrome, future research directions are proposed to advance the study of intelligent analysis systems for childhood epileptic syndrome and alleviate the negative impact of the disease.

Key words： Childhood Epileptic Syndrome Biomarker Electroencephalogram Intelligent Analysis Expert System

Received: 01 November 2023

ZTFLH:	TN911.7
	TP183
	R742.1

Fund:

National Key Research and Development Program of China(No.2021YFE0100100,2021YFE0205400), Joint Funds of National Natural Science Foundation of China(No.U1909209), Key Program of Natural Science Foundation of Zhejiang Province(No.LZ24F030010), Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202249784)

Corresponding Authors: CAO Jiuwen, Ph.D., professor. His research interests include deep learning, neural networks and medical signal processing.

About author:: ZHENG Runze, Ph.D. candidate. His research interests include intelligent signal ana-lysis and processing. FENG Yuanmeng, Ph.D. candidate. His research interests include intelligent signal analysis and processing. HU Dinghan, Ph.D., lecturer. Her research interests include machine learning and EEG signal analysis and processing. JIANG Tiejia, Master, deputy director. His research interests include quantitative analysis of electrophysiological signals in children's neurological diseases. GAO Feng, Master, professor. His research interests include electrophysiological characteristics and etiology of epilepsy.

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Cite this article:

ZHENG Runze,FENG Yuanmeng,HU Dinghan等. Intelligent Analysis of Childhood Epileptic Syndrome: Overview and Prospect[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(2): 144-161.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202402004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I2/144

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