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

• Published in: Computers (Basel) Vol. 9; no. 4; pp. 85 - 14
• Main Authors: Quintero-Rincón, Antonio, Muro, Valeria, D’Giano, Carlos, Prendes, Jorge, Batatia, Hadj
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020; MDPI
• Subjects: Classification; Computer Science; Convulsions & seizures; Decision trees; Decomposition; Discriminant analysis; EEG; Electroencephalography; Epilepsy; Error analysis; generalized Gaussian distribution; k-nearest neighbors classifier; Machine learning; Maximum likelihood method; Medical Imaging; Morlet wavelet; Morphology; Normal distribution; Patients; Segments; Signal and Image Processing; Signal processing; spike-and-wave; Statistical analysis; Statistical models; Variance; Wavelet transforms; Spike-and-wave; K-nearest neighbors classifier; Morlet wavelet; Generalized Gaussian distribution; EEG; Epilepsy
• ISSN: 2073-431X; 2073-431X
• DOI: 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.