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Neuroscience and Biomedical Engineering (Discontinued)

Editor-in-Chief

ISSN (Print): 2213-3852
ISSN (Online): 2213-3860

Research Article

Wavelet Transform Based Algorithm for Automatic Detection of Patient- Specific Single Spike-and-Wave Discharges in Simulated Real-Time Conditions

Author(s): Filipp Polivannyi, Tomohiko Igasaki*, Nobuki Murayama and Ryuji Neshige

Volume 4 , Issue 4 , 2016

Page: [263 - 272] Pages: 10

DOI: 10.2174/2213385204666160923123909

open access plus

Abstract

Background: Transcranial magnetic stimulation applied at the appearance of spike-and-wave discharges in patients’ electroencephalograms may inhibit seizures. The prospect of transcranial magnetic stimulation holds much promise as a noninvasive treatment method for epileptic seizures, and the development of a system for the automatic detection of spike-and-wave discharges would facilitate implementation of this treatment method. However, the variety of waveforms and the appearance in the electroencephalography signal of waveforms similar to spike-and-wave discharges, called pseudo-spikeand- wave discharges, makes successful detection difficult to achieve.

Objective: The aim of the current research was to develop an algorithm for the online detection of spikeand- wave discharges in epileptic patients’ electroencephalograms.

Methods: In this study, a wavelet transform was used as the backbone for the algorithm. A clinician extracted data from a thirty-minute four-lead electroencephalography data recording, comprising fifty-four spike-and-wave discharge samples and fifteen pseudo-spike-and-wave discharge samples.

Results: The simulated online detection method distinguished spike-and-wave discharges from pseudospike- and-wave discharges. However, a few cases of over-detection occurred, which has implications for the specificity and safety of the developed algorithm.

Conclusion: The performance of a newly developed algorithm was reported. A visual analysis of the spike-and-wave discharges and pseudo-waveforms, as well as a time-frequency domain analysis, revealed features that make optimal detection of spike-and-wave discharge waveforms from other oscillations in electroencephalography recordings possible at a threshold level.

Keywords: Epilepsy, spike-and-wave discharges (SWDs), wavelet transform, skeleton waveform, wavelet spectrum coefficients matrix, correlation, electroencephalography.

Graphical Abstract

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