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

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.

authors

• Tafreshi, Reza

published proceedings

• Annu Int Conf IEEE Eng Med Biol Soc

author list (cited authors)

• Zandi, A. S., Tafreshi, R., Dumont, G. A., Ries, C. R., MacLeod, B. A., & Puil, E.

citation count

• 5

complete list of authors

• Zandi, Ali Shahidi||Tafreshi, Reza||Dumont, Guy A||Ries, Craig R||MacLeod, Bernard A||Puil, Ernie

publication date

• August 2007

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2375-7477ISSN  Journal

keywords

• Algorithms
• Data Interpretation, Statistical
• Electroconvulsive Therapy
• Electroencephalography
• Equipment Design
• False Positive Reactions
• Fourier Analysis
• Humans
• Models, Statistical
• Seizures
• Sensitivity And Specificity
• Signal Processing, Computer-Assisted

PubMed Central ID

• 18002357

Digital Object Identifier (DOI)

• 10.1109/IEMBS.2007.4352691

start page

• 1916

end page

• 1919

volume

• 2007

URL

• http://dx.doi.org/10.1109/iembs.2007.4352691