NDLI: Periodogram pattern feature-based seizure detection algorithm using optimized hybrid model of MLP and Ant Colony

Content Provider	IEEE Xplore Digital Library
Author	Behnam, M. Pourghassem, H.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Islamic Azad Univ., Najafabad, Iran (Behnam, M.; Pourghassem, H.)
Abstract	Seizure detection with EEG analysis in the form of offline is an application of brain signal processing. In this paper, a seizure detection algorithm using pattern features of periodogram and an optimized hybrid model based on Ant Colony Optimization (ACO) and Multi-Layer Perceptron (MLP) neural network is proposed. In this algorithm, with introducing a novel feature extraction method, a feature vector based on periodogram is extracted. For this purpose, we define a novel color map of periodogram pattern based on windowing method and modified periodogram. Statistical and frequency features are computed for each one of five rhythms in EEG signals. These rhythms and features are extracted by using a novel scenario that is called Periodogram Pattern Feature (PPF). In this scenario, at first by making a color pattern of periodogram as an estimation of the spectral density, for each signal, a Time-Frequency layout is created. This pattern is divided to some epochs for decomposing the brain rhythms. Also, to optimize the feature vector, ACO algorithm in combination with MLP is used. At last, a perfect set of features with 5 valid items has provided. Moreover, seizure from non-seizure signal has been classified with accuracy of 94.4% using the final optimized features.
Starting Page	32
Ending Page	37
File Size	998270
Page Count	6
File Format	PDF
e-ISBN	9781479919727
DOI	10.1109/IranianCEE.2015.7146178
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-10
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Electrical engineering Conferences Modified periodogram Seizure attack Ant Colony algorithm (ACO) EEG signal Spectral density
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Epileptic seizure detection from EEG signal using Discrete Wavelet Transform and Ant Colony classifier

Epileptic Seizure Detection From EEG Signal Using Discrete Wavelet Transform and Ant Colony Classifier

Epileptic seizure prediction using angle method

Sudoku Solver using ANT-Colony-Optimization Algorithm

Studies on the Dynamics of Ant Colony Optimization Algorithms (2002)

The effectiveness of dynamic ant colony tuning †.

An Optimal Feature Set for Seizure Detection Systems for Newborn EEG Signals

An edge detection technique using hybrid Ant Colony Optimization-genetic algorithm

EEG signal classification for epileptic seizure detection using optimized feature selection algorithms

Periodogram pattern feature-based seizure detection algorithm using optimized hybrid model of MLP and Ant Colony

Similar Documents

Epileptic seizure detection from EEG signal using Discrete Wavelet Transform and Ant Colony classifier

Epileptic Seizure Detection From EEG Signal Using Discrete Wavelet Transform and Ant Colony Classifier

Epileptic seizure prediction using angle method

Sudoku Solver using ANT-Colony-Optimization Algorithm

Studies on the Dynamics of Ant Colony Optimization Algorithms (2002)

The effectiveness of dynamic ant colony tuning †.

An Optimal Feature Set for Seizure Detection Systems for Newborn EEG Signals

An edge detection technique using hybrid Ant Colony Optimization-genetic algorithm

EEG signal classification for epileptic seizure detection using optimized feature selection algorithms

Periodogram pattern feature-based seizure detection algorithm using optimized hybrid model of MLP and Ant Colony