NDLI: Seizure detection using regression tree based feature selection and polynomial SVM classification

Content Provider	IEEE Xplore Digital Library
Author	Zisheng Zhang Parhi, K.K.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA (Zisheng Zhang; Parhi, K.K.)
Abstract	This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients with low hardware complexity and low power consumption. In the proposed approach, we first compute the spectrogram of the input fragmented EEG signals from three or four electrodes. Each fragmented data clip is one second in duration. Spectral powers and spectral ratios are then extracted as features. The features are then subjected to feature selection using regression tree. The selected features are then subjected to a polynomial support vector machine (SVM) classifier with degree of 2. The algorithm is tested using the intra-cranial EEG (iEEG) from the UPenn and Mayo Clinic's Seizure Detection Challenge database. It is shown that the proposed algorithm can achieve a sensitivity of 100.0%, an average area under curve (AUC) of 0.9818, a mean detection horizon of 5.8 seconds, and a specificity of 99.9% on using half of the training data for classification. The proposed approach also achieved a mean AUC of seizure detection and early seizure detection of 0.9136 on the testing data.
Starting Page	6578
Ending Page	6581
File Size	1018488
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319900
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Electrodes Electroencephalography Support vector machines Epilepsy Testing Regression tree analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

Seizure prediction using polynomial SVM classification

A low complexity seizure prediction algorithm

Automatic epileptic seizure onset detection using Matching Pursuit: A case study

Seizure prediction with bipolar spectral power features using Adaboost and SVM classifiers

Channel selection for epilepsy seizure prediction method based on machine learning

Impact of Patient-Specificity on Seizure Onset Detection Performance

A boosted cascade for efficient epileptic seizure detection

Seizure prediction using cost-sensitive support vector machine

Classification of schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM)

Seizure detection using regression tree based feature selection and polynomial SVM classification

Similar Documents

Seizure prediction using polynomial SVM classification

A low complexity seizure prediction algorithm

Automatic epileptic seizure onset detection using Matching Pursuit: A case study

Seizure prediction with bipolar spectral power features using Adaboost and SVM classifiers

Channel selection for epilepsy seizure prediction method based on machine learning

Impact of Patient-Specificity on Seizure Onset Detection Performance

A boosted cascade for efficient epileptic seizure detection

Seizure prediction using cost-sensitive support vector machine

Classification of schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM)

Seizure detection using regression tree based feature selection and polynomial SVM classification