Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

Antonio Quintero Rincón; Hadj Batatia; Jorge Prende; Valeria Muro; Carlos D'Giano

doi:10.20944/preprints202010.0616.v1

Preprint

Article

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

This version is not peer-reviewed.

Antonio Quintero Rincón^*

Hadj Batatia^*

,Jorge Prende,Valeria Muro,Carlos D'Giano

Antonio Quintero Rincón^*

Hadj Batatia^*

,Jorge Prende,Valeria Muro,Carlos D'Giano

This version is not peer-reviewed.

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Submitted:

28 October 2020

Posted:

29 October 2020

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Abstract

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the Morlet 1-D decomposition is applied. The generalized Gaussian distribution (GGD) statistical model is fitted to the resulting wavelet coefficients. A k-nearest neighbors (k-NN) self-supervised classifier is trained using the GGD parameters to detect the spike-and-wave pattern. Experiments were conducted using 106 spike-and-wave signals and 106 non-spike-and-wave signals for training and another 96 annotated EEG segments from six human subjects for testing. The proposed SWD classification methodology achieved 95 % sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These results set the path to new research to study causes underlying the so-called absence epilepsy in long-term EEG recordings.

Keywords:

Spike-and-wave

;

Generalized Gaussian distribution

;

EEG

;

Morlet wavelet

;

k-nearest neighbors classifier

;

Epilepsy

Subject:

Computer Science and Mathematics - Algebra and Number Theory

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

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