Pattern Recognition Techniques for the Detection of Epileptic Transients in EEG

Epileptic transients (ET) in EEG, characterized by their amplitude and duration, are compared to background activity by means of cluster analysis in a two-dimensional amplitude-duration sample space. Decisions between ET and background are then carried out by assigning an optimum boundary between the clusters. By filtering the EEG signal using linear prediction followed by second differentiation, a useful degree of cluster separation is achieved. The probability of correct decisions is thus enhanced. Data for several patients are shown.