Artifact Removal using Elliptic Filter and Classification using 1D-CNN for EEG signals

被引：0

作者：

Nagabushanam, P. ^{[1
]}

George, S. Thomas ^{[2
]}

Davu, Praharsha ^{[3
]}

Bincy, P. ^{[3
]}

Naidu, Meghana ^{[3
]}

Radha, S. ^{[3
]}

机构：

[1] Karunya Inst Technol & Sci, Dept EEE, Cbe, India

[2] Karunya Inst Technol & Sci, Dept EIE, Cbe, India

[3] Karunya Inst Technol & Sci, Dept ECE, Cbe, India

来源：

2020 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATION SYSTEMS (ICACCS) | 2020年

关键词：

EEG; 1D-CNN; Artifact removal; elliptic filter;

D O I：

10.1109/icaccs48705.2020.9074287

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Convolution plays major role in deep learning. CNN is the basic architecture and most of the other deep learning architectures ae built based on CNN. However, 1D-CNN is suitable for EEG signals as it is time-series one dimensional characteristic signal. In this paper, we have proposed a 6-layer 1D-CNN to analyze the classification of EEG. Elliptic filter helps to filter Multivariate EEG signal of sleep apnea patient and then classify sleep stages using CNN. Simulations are carried out using Mat Lab.

引用

页码：551 / 556

页数：6

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