The bi-directional spike detection in EEG using mathematical morphology and wavelet transform

Epileptic EEG often contains abnormal spiky activity which is diagnostically important. It is proposed that the multi-resolution wavelet transform with mathematical morphology can be used to detect and extract this activity. Differentiating the geometrical characteristics between spikes and normal EEG activity, the process extracts the target patterns from the EEG data in the multi-resolution domains. Morphological analysis utilizes analytic operations based on a pre-defined structuring element (SE) targeted to specific signal features. In our case the SE is defined as a disk to measure the difference in smoothness between the two components. Discrete wavelet transforms are applied to construct the processed signal. The multi-resolution property of the wavelet transform adapts well to the time-invariant nature of the signal. Combining mathematical morphology and wavelet transforms, this method successfully separates the background activity and transient phenomenon from epileptic EEG. Although the morphological operation is a non-linear process, we show that, with the selected structuring element, this approach has ability to detect both positive and negative going spikes identically.