A deep learning based ensemble learning method for epileptic seizure prediction

人工智能 计算机科学 癫痫 卷积神经网络 深度学习 癫痫发作 集成学习 机器学习 支持向量机 脑电图 模式识别(心理学) 分类器(UML) 特征(语言学) 心理学 神经科学 哲学 语言学
作者
Syed Muhammad Usman,Shehzad Khalid,Sadaf Bashir
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:136: 104710-104710 被引量:108
标识
DOI:10.1016/j.compbiomed.2021.104710
摘要

In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
可靠的如之完成签到,获得积分10
1秒前
专注棒棒糖完成签到 ,获得积分10
1秒前
1秒前
Lily发布了新的文献求助10
1秒前
2秒前
YZQ发布了新的文献求助10
3秒前
黑咖啡完成签到,获得积分10
3秒前
Liufgui应助可靠的如之采纳,获得10
5秒前
科研通AI2S应助阿俊采纳,获得10
6秒前
7秒前
9秒前
11秒前
11秒前
JamesPei应助YZQ采纳,获得10
12秒前
Orange应助邪恶花生米采纳,获得10
12秒前
weijie发布了新的文献求助10
12秒前
hf完成签到,获得积分10
12秒前
12秒前
14秒前
量子星尘发布了新的文献求助30
15秒前
硅负极完成签到,获得积分10
15秒前
zzt发布了新的文献求助10
15秒前
16秒前
Dr.Yang发布了新的文献求助10
17秒前
19秒前
刻苦的秋柔完成签到,获得积分10
21秒前
意大利种马完成签到,获得积分20
22秒前
orixero应助写得出发的中采纳,获得10
24秒前
刘雨森完成签到 ,获得积分10
25秒前
坦率白萱应助littleblack采纳,获得10
26秒前
香蕉觅云应助意大利种马采纳,获得10
27秒前
ZS完成签到,获得积分10
27秒前
帅哥的事情少管完成签到,获得积分10
28秒前
littlestone完成签到,获得积分10
29秒前
NexusExplorer应助ShuXU采纳,获得10
31秒前
果果完成签到,获得积分10
31秒前
项绝义完成签到,获得积分10
32秒前
32秒前
空古悠浪发布了新的文献求助20
32秒前
32秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988920
求助须知:如何正确求助?哪些是违规求助? 3531290
关于积分的说明 11253247
捐赠科研通 3269903
什么是DOI,文献DOI怎么找? 1804830
邀请新用户注册赠送积分活动 882027
科研通“疑难数据库(出版商)”最低求助积分说明 809052