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Title
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Detection of epileptic convulsions from accelerometry signals through machine learning approach
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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2014 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
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Source (book)
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IEEE International Workshop on Machine Learning for Signal Processing, (MLSP), SEP 21-24, 2014, Reims, FRANCE
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Publication
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New york
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Ieee
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2014
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ISSN
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2161-0363
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ISBN
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978-1-4799-3694-6
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Volume/pages
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(2014)
, 6 p.
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ISI
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000393407800019
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Full text (publisher's version - intranet only)
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