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Spike-and-wave discharge (SWD) pattern classification inelectroencephalography (EEG) signals is a key problem in signal processing. Itis particularly important to develop a SWD automatic detection method inlong-term EEG recordings since the task of marking the patters manually is timeconsuming, difficult and error-prone. This paper presents a new detectionmethod with a low computational complexity that can be easily trained ifstandard medical protocols are respected. The detection procedure is asfollows: First, each EEG signal is divided into several time segments and foreach time segment, the Morlet 1-D decomposition is applied. Then threeparameters are extracted from the wavelet coefficients of each segment: scale(using a generalized Gaussian statistical model), variance and median. This isfollowed by a k-nearest neighbors (k-NN) classifier to detect thespike-and-wave pattern in each EEG channel from these three parameters. A totalof 106 spike-and-wave and 106 non-spike-and-wave were used for training, while69 new annotated EEG segments from six subjects were used for classification.In these circumstances, the proposed methodology achieved 100% accuracy. Theseresults generate new research opportunities for the underlying causes of theso-called absence epilepsy in long-term EEG recordings.

A novel spike-and-wave automatic detection in EEG signals