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

• Autores: Quintero-Rincón, Antonio; Muro, Valeria; D’Giano, Carlos; Prendes, Jorge; Batatia, Hadj
• Año de publicación: 2020
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Computers Vol.9, No.4, 2020
• Materia: EPILEPSIA; ELECTROENCEFALOGRAFIA; ONDAS ENCEFALICAS; TECNICAS DE DIAGNOSTICO NEUROLOGICO
• Institución: Pontificia Universidad Católica Argentina

Fil: Quintero-Rincón, Antonio. Pontificia Universidad Católica Argentina; Argentina
Fil: Quintero-Rincón, Antonio. Fleni. Fundación para la Lucha contra la Enfermedad Neurológica Pediátrica; Argentina
Fil: Muro, Valeria. Fleni. Fundación para la Lucha contra la Enfermedad Neurológica Pediátrica; Argentina
Fil: D’Giano, Carlos. Fleni. Fundación para la Lucha contra la Enfermedad Neurológica Pediátrica; Argentina
Fil: Prendes, Jorge. Université Toulouse. Institut de Recherche en Informatique; Francia
Fil: Batatia, Hadj. Universidad Heriot-Watt de Dubái; Emiratos Árabes Unidos
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.