Elimination of pseudo-HFOs in iEEG using sparse representation and Random Forest classifier

• Published in: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2022; pp. 4888 - 4891
• Main Authors: Besheli, Behrang Fazli, Sha, Zhiyi, Henry, Thomas, Gavvala, Jay R, Gurses, Candan, Karamursel, Sacit, Ince, Nuri F.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2022
• Subjects: Detectors; Dictionaries; Electroencephalography - methods; Hafnium oxide; Humans; Location awareness; Matching pursuit algorithms; Recording; Seizures; Sensitivity
• ISSN: 2694-0604
• DOI: 10.1109/EMBC48229.2022.9871447

High-Frequency Oscillation (HFO) is a promising biomarker of the epileptogenic zone. However, sharp artifacts might easily pass the conventional HFO detectors as real HFOs and reduce the seizure onset zone (SOZ) localization. We hypothesize that, unlike pseudo-HFOs, which originates from artifacts with sharp changes or arbitrary waveform characteristic, real HFOs could be represented by a limited number of oscillatory waveforms. Accordingly, to distinguish true ones from pseudo-HFOs, we established a new classification method based on sparse representation of candidate events that passed an initial detector with high sensitivity but low specificity. Specifically, using the Orthogonal Matching Pursuit (OMP) and a redundant Gabor dictionary, each event was represented sparsely in an iterative fashion. The approximation error was estimated over 30 iterations which were concatenated to form a 30-dimensional feature vector and fed to a random forest classifier. Based on the selected dictionary elements, our method can further classify HFOs into Ripples (R) and Fast Ripples (FR). In this scheme, two experts visually inspected 2075 events captured in iEEG recordings from 5 different subjects and labeled them as true-HFO or Pseudo-HFO. We reached 90.22% classification accuracy in labeled events and a 21.16% SOZ localization improvement compared to the conventional amplitude-threshold-based detector. Our sparse representation framework also classified the detected HFOs into R and FR subcategories. We reached 91.24% SOZ accuracy with the detected \mathrm{R}+\text{FR} events. Clinical Relevance---This sparse representation framework establishes a new approach to distinguish real from pseudo-HFOs in prolonged iEEG recordings. It also provides reliable SOZ identification without the selection of artifact-free segments.