Exploration of instantaneous amplitude and frequency features for epileptic seizure prediction

• Main Authors: Ning Wang, Lyu, M. R.
• Format: Conference Proceeding
• Language: English
• Subjects: electroencephalogram signal representation; Electroencephalography; Epilepsy; Epileptic seizure prediction; Feature extraction; Frequency modulation; instantaneous amplitude and frequency modulation features; Prediction algorithms; Sensitivity; Vectors
• DOI: 10.1109/BIBE.2012.6399691

For the purpose of effective epileptic seizure prediction, this paper presents a new representation for the electroencephalogram (EEG) signal by recurring to their primary amplitude and frequency components. This formulation is then applied on an epoch-by-epoch basis to the preictal and interictal states of EEG signals in order to extract the most dominant amplitude and frequency characteristics. Through inspecting and identifying the distinctive EEG signal changes and stage transitions revealed by these extracted feature vectors, upcoming epileptic seizures could be predicted in due course. Machine learning approaches have been employed to construct patient-specific classifiers that can divide the extracted feature vectors into preictal and interictal groups. In this context, our work is distinguished from most currently adopted feature extraction process which employs time-consuming high-dimensional parameter sets. We have made special efforts to derive discriminative and comprehensive features for the front-end of the epileptic seizure prediction algorithm. To reduce false alarms due to trivial signal fluctuation, a simple yet effective post-processing step is incorporated thereafter. Performance of the prediction algorithm is assessed through out-of-sample evaluation on the intracranial EEG (iEEG) recordings provided by the publicly available Freiburg data set. It has been shown by simulation results that the proposed feature estimation method leads to promising prediction results in terms of sensitivity and specificity. In particular, only four out of the 83 seizures across all the patients included in our experiment were missed by the prediction, which means that a sensitivity as high as 95.2% has been achieved.