Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals

作     者：Jian Liu Yipeng Du Xiang Wang Wuguang Yue Jim Feng 

作者机构：University of Science and Technology BeijingBeijing100083China Hwa Create Co.LtdBeijing100193China Amphenol Global Interconnect SystemsSan JoseCA 95131US 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：1995-2011页

核心收录：

学科分类：1002[医学-临床医学] 10[医学] 

基　　金：This work is supported by Fundamental Research Funds for the Central Universities(Grant No.FRF-TP-19-006A3) 

主　　题：Deep learning automated machine learning EEG seizure detection 

摘      要：Epilepsy is a common neurological disease and severely affects the daily life of *** automatic detection and diagnosis system of epilepsy based on electroencephalogram(EEG)is of great significance to help patients with epilepsy return to normal *** the development of deep learning technology and the increase in the amount of EEG data,the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted ***,the neural architecture design for epilepsy EEG analysis is time-consuming and laborious,and the designed structure is difficult to adapt to the changing EEG collection environment,which limits the application of the epilepsy EEG automatic detection *** this paper,we explore the possibility of Automated Machine Learning(AutoML)playing a role in the task of epilepsy EEG *** apply the neural architecture search(NAS)algorithm in the AutoKeras platform to design the model for epilepsy EEG analysis and utilize feature interpretability methods to ensure the reliability of the searched *** experimental results show that the model obtained through NAS outperforms the baseline model in *** searched model improves classification accuracy,F1-score and Cohen’s kappa coefficient by 7.68%,7.82%and 9.60%respectively than the baseline ***,NASbased model is capable of extracting EEG features related to seizures for classification.