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
|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|
|Publication status||Published - 2008|
Nijsen, T. M. E., Aarts, R. M., Arends, J. B. A. M., & Cluitmans, P. J. M. (2008). Automated detection of tonic seizures using 3-D accelerometry. In J. Vander Sloten, P. Verdonck, & M. Nyssen (Eds.), 4th European Congress of the International Federation for Medical and Biological Engineering (IFMBE), Antwerpen, Belgium, 23-27 Novermber 2008 (pp. 188-191). Berlin: Springer.