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Statistical model-based classification to detect patient-specific spike-and-wave in EEG signals computers2020

Quintero Rincón, Antonio and Muro, Valeria and D'Giano, Carlos and Prendes, Jorge and Batatia, Hadj Statistical model-based classification to detect patient-specific spike-and-wave in EEG signals computers2020. (2020) Computers, 9(4) (85). 1-14.

(Document in English)

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Official URL: https://doi.org/10.3390/computers9040085


Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.

Item Type:Article
Additional Information:This is an open access article under the CC BY-NC-ND license : https://creativecommons.org/licenses/by-nc-nd/4.0/. Thanks to MDPI editor. The definitive version of this document is available at: https://www.mdpi.com/2073-431X/9/4/85
HAL Id:hal-02990761
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Other partners > Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia - FLENI (ARGENTINA)
Other partners > Heriot-Watt University (UNITED KINGDOM)
Other partners > Pontificia Universidad Católica Argentina - UCA (ARGENTINA)
Laboratory name:
Deposited On:05 Nov 2020 14:56

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