Publication
Title
Detection of epileptic convulsions from accelerometry signals through machine learning approach
Author
Abstract
A seizure detection system in the non-clinical environment would enable long-term monitoring and give better insights into the number of seizures and their characteristics. Moreover, an alarm at seizure onset is important for alerting the parents or care-givers so they could comfort the child and optionally give the treatment. Therefore, we developed a patient-independent automatic algorithm for registration and detection of (tonic-) clonic seizures based on four accelerometers attached to the wrists and ankles. The objective is to classify two second epochs as seizure or non-seizure epochs employing supervised learning techniques. Starting from 140 features found in similar publications, a filter method based on mutual information is applied to remove irrelevant and redundant features. A least-squares support vector machine classifier is used to distinguish seizure and non-seizure epochs based on the selected features. For seizures longer than 30 seconds, median sensitivity of 100%, false detection rate of 0.39 h(-1) and alarm delay of 15.2 s over all patients are reached.
Language
English
Source (journal)
2014 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
Source (book)
IEEE International Workshop on Machine Learning for Signal Processing, (MLSP), SEP 21-24, 2014, Reims, FRANCE
Publication
New york : Ieee , 2014
ISSN
2161-0363
ISBN
978-1-4799-3694-6
Volume/pages
(2014) , 6 p.
ISI
000393407800019
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
Compact representation of biomedical signals.
Dynamische systemen, regeling en optimalisering
TRANSACT: Transforming Magnetic Resonance Spectroscopy into a Clinical Tool
BIOTENSORS: Biomedical Data Fusion using Tensor based Blind Source Separation
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.08.2018
Last edited 17.11.2021
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