Abstract
A first approach is presented for the detection of
accelerometry (ACM) patterns associated with tonic seizures.
First it is shown that during tonic seizures the typical ACMpattern
is mainly caused by change of position towards the
field of gravity and that the acceleration caused by movement
is negligible. To this end a mechanical model of the arm and
physiological information about muscle contraction during tonic
seizures are used. Then six features are computed that represent
the main characteristics of ACM-patterns associated with tonic
seizures. Linear discriminant analysis is used for classification.
For training and evaluation ACM-data are used from mentally
retarded patients with severe epilepsy. It was possible to detect
tonic seizures with a success rate around 0.80 and with a
positive predictive value (PPV) of 0.35. For off-line analysis
this is acceptable, especially when 42 % of the false alarms are
actually motor seizures of another type. The missed seizures,
were not clearly visible in the ACM-signal. For these seizures
additional ACM-sensors or a combination with other sensor
types might be necessary. The results show that our approach
is useful for the automated detection of tonic seizures and that
it is a promising contribution in a complete multi-sensor seizure
detection setup.
I
Original language | English |
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Title of host publication | 4th European Congress of the International Federation for Medical and Biological Engineering (IFMBE), Antwerpen, Belgium, 23-27 Novermber 2008 |
Editors | Jos Vander Sloten, Pascal Verdonck, Marc Nyssen |
Place of Publication | Berlin |
Publisher | Springer |
Pages | 188-191 |
ISBN (Print) | 978-3-540-89207-6 |
Publication status | Published - 2008 |