Unsupervised Detection of High-Frequency Oscillations Using Time-Frequency Maps and Computer Vision - Donos, Cristian; Mîndruţă, Ioana; Barborica, Andrei (Frontiers in Neuroscience, 2020)
High-frequency oscillations >80 Hz (HFOs) have unique features distinguishing them from spikes and artefactual components that can be well evidenced in the time-frequency representations. We introduce an unsupervised HFO detector that uses computer-vision algorithms to detect HFO landmarks on 2D time-frequency maps. To validate the detector, we introduce an analytical model of the HFO based on a sinewave having a Gaussian envelope, for which analytical equations in time-frequency space can be derived, allowing us to establish a direct correspondence between common HFO detection criteria in the time domain with the ones in the frequency domain, used by the computer-vision detection algorithm. The detector identifies potential HFO events on the time-frequency representation, which are classified as true HFOs if criteria regarding the HFO’s frequency, amplitude and duration are met. The detector is validated on simulated HFOs according to the analytical model, in the presence of noise, with different signal-to-noise ratios (SNR) ranging -9 to 0 dB. The detector’s sensitivity was 0.64 at a SNR of -9dB, 0.98 at -6dB and >0.99 at -3dB and 0dB, while its positive prediction value was >0.95, regardless of the SNR. Using the same simulation dataset, out detector is benchmarked against four previously published HFO detectors. The F-measure, a combined metric that takes into account both sensitivity and positive prediction value, was used to compare detection algorithms. Our detector surpassed the other detectors at -6, -3 and 0 dB, and had the second best F-score at -9dB SNR after the MNI detector (0.77 vs. 0.83). The ability to detect HFOs in clinical recordings has been tested on a set of 36 intracranial EEG channels in 6 patients, with 89% of the detections being validated by two independent reviewers. The results demonstrate that the unsupervised detection of HFOs based on their 2D features in time-frequency maps is feasible and has a performance comparable or better than the most used HFO detectors.