| 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) |
|
|
| | |
|