Detection of high-frequency oscillations using time-frequency analysis

High-frequency oscillations (HFOs) are a new biomarker for identifying the epileptogenic zone. Mapping HFO-generating regions can improve the precision of resection sites in patients with refractory epilepsy. However, detecting HFOs remains challenging, and their clinical features are not yet fully defined. Visual identification of HFOs is time-consuming, labor-intensive, and subjective. As a result, developing automated methods to detect HFOs is critical for research and clinical use. In this study, we developed a novel method for detecting HFOs in the ripple and fast ripple frequency bands (80-500 Hz). We validated it using both controlled datasets and data from epilepsy patients. Our method employs an unsupervised clustering technique to categorize events extracted from the time-frequency domain using the S-transform. The proposed detector differentiates HFO events from spikes, background activity, and artifacts. Compared to existing detectors, our method achieved a sensitivity of 97.67%, a precision of 98.57%, and an F-score of 97.78% on the controlled dataset. In epilepsy patients, our results showed a stronger correlation with surgical outcomes, with a ratio of 0.73 between HFO rates in resected versus non-resected contacts. The study confirmed previous findings that HFOs are promising biomarkers of epileptogenicity in epileptic patients. Removing HFOs, especially fast ripples, leads to seizure freedom, while remaining HFOs lead to seizure recurrence.