Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Mar;41(1):81-94.
doi: 10.1007/s13246-017-0610-y. Epub 2017 Dec 20.

Epileptic seizure detection in EEG signal using machine learning techniques

Abeg Kumar Jaiswal  1 , Haider Banka  2
Affiliations

Epileptic seizure detection in EEG signal using machine learning techniques

Abeg Kumar Jaiswal et al. Australas Phys Eng Sci Med. 2018 Mar.

Abstract

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Keywords: Classification; Cross-subpattern correlation-based PCA (SubXPCA); Electroencephalogram (EEG) signal; Feature extraction; Principal component analysis (PCA); Subpattern based PCA (SpPCA); Support Vector Machine (SVM).

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

LinkOut - more resources

  NODES
twitter 2