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

[ Vol. 4 , Issue. 4 ]


Filipp Polivannyi, Tomohiko Igasaki*, Nobuki Murayama and Ryuji Neshige   Pages 263 - 272 ( 10 )


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.


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


Department of Human and Environmental Informatics, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Research Field of Biomedical and Welfare Engineering, Division of Environmental Science, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Department of Human and Environmental Informatics, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Neshige Clinic of Neurology and Internal Medicine, Kurume

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