Automatic High-Frequency Oscillations Detection Using Time-Frequency Analysis

The role of high-frequency oscillations (HFO) has been established in a multitude of the brain functions such as retrieval and consolidation of memory. Moreover, HFOs have been identified as a biomarker for pathological brain conditions, including epileptogenicity. Therefore, there has been a continuous effort to reliably detect and characterize HFOs. Here, we present an unsupervised HFO detector using characteristics of signals in the time-frequency domain obtained by continuous wavelet transform. By using L1 normalization for continuous wavelet transform, we improved the detection of HFOs without the need to normalize time-frequency maps. The elimination of normalizing the time-frequency maps reduces the computational cost of the analysis. We used two different benchmark datasets available in the literature to validate our proposed automatic HFO detector. The results demonstrate that our detector outperforms other commonly available HFO detectors including those that use timefrequency maps. Our HFO detector shows superior performance especially when signal-to-noise ratio (SNR) is low. Moreover, our detector can simultaneously detect artifacts, physiological spikes, and provide useful information about the HFOs such as their dominant frequency of oscillation, their average amplitude and their duration. This information can later be utilized to stratify HFOs for further analysis. Changes in HFO characteristics may be utilized as biomarkers in pathological conditions such as posttraumatic epilepsy.