Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 28 October 2020 / Approved: 29 October 2020 / Online: 29 October 2020 (14:05:54 CET)
(This article belongs to the Research Topic EUSAR 2020—Preprints)

How to cite: Quintero Rincón, A.; Batatia, H.; Prende, J.; Muro, V.; D'Giano, C. Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals. Preprints 2020, 2020100616 (doi: 10.20944/preprints202010.0616.v1). Quintero Rincón, A.; Batatia, H.; Prende, J.; Muro, V.; D'Giano, C. Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals. Preprints 2020, 2020100616 (doi: 10.20944/preprints202010.0616.v1).

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.

Subject Areas

Spike-and-wave; Generalized Gaussian distribution; EEG; Morlet wavelet; k-nearest neighbors classifier; Epilepsy

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