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. Preprints2020, 2020100616. https://doi.org/10.20944/preprints202010.0616.v1
APA Style
Quintero Rincón, A., Batatia, H., Prende, J., Muro, V., & D'Giano, C. (2020). Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals. Preprints. https://doi.org/10.20944/preprints202010.0616.v1
Chicago/Turabian Style
Quintero Rincón, A., Valeria Muro and Carlos D'Giano. 2020 "Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals" Preprints. https://doi.org/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.
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
