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.