Neuroscience and Biomedical Engineering

Jinglong Wu  
Graduate School of Natural Science
and Technology, Okayama University
Okayama
Japan

Kewei Chen
Banner Alzheimer Institute
Phoenix, AZ
USA

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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, Ryuji Neshige.

Graphical Abstract:


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.

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Article Details

VOLUME: 4
ISSUE: 4
Year: 2016
Page: [263 - 272]
Pages: 10
DOI: 10.2174/2213385204666160923123909