Detection of High Frequency Oscillations (HFOs) in the 80–500 Hz range in epilepsy recordings using decision tree analysis

Discrete High Frequency Oscillations (HFOs) in the range of 80-500 Hz have recently received attention as a promising reliable biomarkers for epileptic activity, both in scalp EEG as well as in intracranial recordings. HFOs are often characterized by variable durations (10-100 ms) and rates of occurrence (17.5 ± 9.5 / min). The total duration of HFOs is extremely small compared to the entire length of the EEG signals to be analyzed which, in the case of intracerebral recordings, are generally acquired over several days and sometimes up to weeks. As a result, visual marking of HFOs events associated with large amounts of EEG data is extremely tedious, inevitably subjective and requires a great deal of mental concentration. Therefore, automatic detection of HFOs can be very useful to propel the clinical use of HFOs as biomarkers of epileptogenic tissue and is crucial when conducting large-scale investigations of HFO activity. As a first step towards robust and reliable automatic detection, we propose in this paper a new method for HFOs detection based on Decision Tree analysis. The performance and added value of the proposed method are evaluated by comparing it with five other previously proposed methods. The HFO detection performances were tested in terms of sensitivity, False Discovery Rate (FDR) and Area Under the ROC Curve (AUC). Our results demonstrate that the decision-tree approach yields low false detection (FDR=8.62 %) but that, in its current implementation, it is not highly sensitive to HFO events (sensitivity=66.96 %). Nevertheless some advantages of the method are discussed and paths for further improvements are outlined.