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