Electroconvulsive therapy: a model for seizure detection by a wavelet packet algorithm

TitleElectroconvulsive therapy: a model for seizure detection by a wavelet packet algorithm
Publication TypeJournal Article
Year of Publication2007
AuthorsZandi, A. S., R. Tafreshi, G. A. Dumont, C. R. Ries, B. A. MacLeod, and E. Puil
JournalConf Proc IEEE Eng Med Biol Soc
Volume2007
Pagination1916-9
Abstract

Electroconvulsive therapy (ECT) is an effective treatment for severe depression. In this paper, we have used an algorithm based on wavelet packet (WP) analysis of EEG signals to detect seizures induced by ECT. After determining dominant frequency bands in the ictal period during ECT, the energy ratio of these bands was computed using the corresponding WP coefficients. This ratio was then used as an index to recognize seizure periods. Four different approaches to detect ECT seizures were employed in 41 EEG recordings from nine patients. Sensitivity in ECT seizure detection ranged from 76 to 95% while the false detection rate ranged from 6 to 13.

URLhttp://dx.doi.org/10.1109/IEMBS.2007.4352691
DOI10.1109/IEMBS.2007.4352691

a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949
Email:

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2020 The University of British Columbia