A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones

This paper presents a new unsupervised detector for automatically detecting high-frequency oscillations (HFOs) using intracranial electroencephalogram (iEEG) signals. This detector does not presuppose a specific number of clusters and has a good performance. First, the HFO candidates are detected by an initial detection method which distinguishes HFOs from background activities. Then, as significant features, fuzzy entropy, short-time energy, power ratio, and spectral centroid of the HFO candidates are investigated and constructed as a feature vector. Finally, the feature vector is used as the input of the fuzzy- ${c}$ -means-quantization-error-modeling-based expectation–maximization-Gaussian mixture model clustering algorithm. This algorithm has the advantages of detecting HFOs and avoiding false detection caused by artifacts. The concentrations of detected HFOs are used to localize epileptic seizure onset zones in epileptic iEEG signal analysis. A comparison shows that our detector provides better localization performance in terms of sensitivity and specificity than five existing detectors.