Electroconvulsive therapy: a model for seizure detection by a wavelet packet algorithm.
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.
Zandi
Ali Shahidi
AS
Department of Electrical & Computer Engineering, The University of British Columbia (UBC), Vancouver, BC, Canada. alis@ece.ubc.ca
Tafreshi
Reza
R
Dumont
Guy A
GA
Ries
Craig R
CR
MacLeod
Bernard A
BA
Puil
Ernie
E
D000465:Algorithms; D003627:Data Interpretation, Statistical; D004565:Electroconvulsive Therapy; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D005189:False Positive Reactions; D005583:Fourier Analysis; D006801:Humans; D015233:Models, Statistical; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted