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

Abstract: 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...

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Autores principales: Quintero-Rincón, Antonio, Muro, Valeria, D’Giano, Carlos, Prendes, Jorge, Batatia, Hadj
Formato: Artículo
Lenguaje:Inglés
Publicado: MDPI 2020
Materias:
EPILEPSIA
ELECTROENCEFALOGRAFIA
ONDAS ENCEFALICAS
TECNICAS DE DIAGNOSTICO NEUROLOGICO
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/10947
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139123456789-10947
record_format dspace
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic EPILEPSIA
ELECTROENCEFALOGRAFIA
ONDAS ENCEFALICAS
TECNICAS DE DIAGNOSTICO NEUROLOGICO
spellingShingle EPILEPSIA
ELECTROENCEFALOGRAFIA
ONDAS ENCEFALICAS
TECNICAS DE DIAGNOSTICO NEUROLOGICO
Quintero-Rincón, Antonio
Muro, Valeria
D’Giano, Carlos
Prendes, Jorge
Batatia, Hadj
Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals
topic_facet EPILEPSIA
ELECTROENCEFALOGRAFIA
ONDAS ENCEFALICAS
TECNICAS DE DIAGNOSTICO NEUROLOGICO
description Abstract: 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.
format Artículo
author Quintero-Rincón, Antonio
Muro, Valeria
D’Giano, Carlos
Prendes, Jorge
Batatia, Hadj
author_facet Quintero-Rincón, Antonio
Muro, Valeria
D’Giano, Carlos
Prendes, Jorge
Batatia, Hadj
author_sort Quintero-Rincón, Antonio
title Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals
title_short Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals
title_full Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals
title_fullStr Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals
title_full_unstemmed Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals
title_sort statistical model-based classification to detect patient-specific spike-and-wave in eeg signals
publisher MDPI
publishDate 2020
url https://repositorio.uca.edu.ar/handle/123456789/10947
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AT prendesjorge statisticalmodelbasedclassificationtodetectpatientspecificspikeandwaveineegsignals
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