AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

被引：12

作者：

Jahan, Sobhana ^{[1
]}

Nowsheen, Farhana ^{[2
]}

Antik, Mahathir Mahmud ^{[2
]}

Rahman, Md. Sazzadur ^{[3
]}

Kaiser, M. Shamim ^{[3
]}

Hosen, A. S. M. Sanwar ^{[4
]}

Ra, In-Ho ^{[5
]}

机构：

[1] Bangladesh Univ Profess, Dept Comp Sci & Engn, Dhaka 1216, Bangladesh

[2] Bangladesh Univ Profess, Dept Informat & Commun Technol, Dhaka 1216, Bangladesh

[3] Jahangirnagar Univ, Inst Informat Technol, Dhaka 1342, Bangladesh

[4] Woosong Univ, Dept Artificial Intelligence & Big Data, Daejeon 34606, South Korea

[5] Kunsan Natl Univ, Sch Comp Informat & Commun Engn, Gunsan 54150, South Korea

来源：

IEEE ACCESS | 2023年 / 11卷

基金：

新加坡国家研究基金会;

关键词：

Electroencephalography; Brain modeling; Epilepsy; Monitoring; Feature extraction; Classification algorithms; Artificial intelligence; Deep learning; Internet of Things; Brain-computer interfaces; brain-computer interface; artificial intelligence; deep learning; EMPIRICAL MODE DECOMPOSITION; CONVOLUTIONAL NEURAL-NETWORK; BRAIN-COMPUTER INTERFACES; FEATURE-EXTRACTION; EEG; CLASSIFICATION; MACHINE; DIAGNOSIS; SELECTION; PATTERN;

D O I：

10.1109/ACCESS.2023.3251105

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Epilepsy is a neurological condition affecting around 50 million individuals worldwide, reported by the World Health Organization. This is identified as a hypersensitive disease by clinical associations. The unique characteristics of Electroencephalography have proven to be stable and universal; therefore, researchers have a lot of credibilities. So, it is the most used test for Epileptic Seizure detection and prediction. This study examines the contributions that have so far been made utilizing Electroencephalography technology to detect, predict, and monitor Epileptic Seizures. We have reviewed around 56 research articles, and those papers are selected from different academic databases. The studies explored various approaches, including Machine Learning, Deep Learning, and the Internet of Things framework. A comprehensive discussion of different classification algorithms is analyzed, and their performances are explored. Furthermore, various open issues of the stated approach are discussed, and potential future works are addressed.

引用

页码：30690 / 30725

页数：36

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