Name
Title | ||
|---|---|---|
Channel-Wise Characterization of High Frequency Oscillations for Automated Identification of the Seizure Onset Zone. |
Abstract | ||
|---|---|---|
High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are a promising clinical biomarker that can help define the epileptogenic regions in the brain. The aim of this study is to characterize the spatial and temporal distribution of HFOs in channel-wise instead of event-level as usual and to develop an automated the seizure onset zone (SOZ) identification by using a support vector machine (SVM) approach on the channel-wise features in a short-term recording. In this work, five consecutive patients with medically intractable epilepsy were enrolled. For each patient, ten-minute segments were defined from two hours of iEEG recordings during sleep state. A total of 17 channel-wise features including 6 rate-based, 6 duration-based, 3 amplitude-based, and 2 power-based features of HFOs were extracted from each 10-min segment, which including ripples (Rs, 80-250 Hz) and fast ripples (FRs, 250-500Hz) were detected automatically using validated detectors. Each channel-wise feature was ranked by using the Student & x2019;s <italic>t-test</italic> method and the most distinctive features were selected to explore the characteristics of HFOs in each channel. A supervised-learning based SVM classifier with the selected channel-wise features or their combinations was developed to identify each channel within the independently clinician-defined SOZ or not. Over 3,816 chanel-10-min segments of iEEG recordings, the evaluated accuracy, sensitivity, and specificity of the proposed approach with the optimal combination of top five ranked features for SOZ identification are 86.6 & x0025;, 73.0 & x0025;, and 94.1 & x0025;, respectively, for ten-fold cross-validation, and 86.0 & x0025;, 79.2 & x0025;, and 91.8 & x0025;, respectively, for the leave-1-out cross-validation. Compared with the recently reported SOZ detectors based on event-wise feature of HFOs, the channel-wise features and the combination with machine learning approach demonstrate its feasibility in SOZ identification with a relative higher performance and potentially reduce the time needed currently for long-term recording and manual inspection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2978290 | IEEE ACCESS |
Keywords | DocType | Volume |
Hafnium oxide,Feature extraction,Electroencephalography,Electrodes,Epilepsy,Machine learning,Epilepsy,high frequency oscillations (HFOs),intracranial electroencephalograms (iEEG),machine learning,seizure onset zone | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dakun Lai | 1 | 0 | 0.34 |
| Xinyue Zhang | 2 | 0 | 0.34 |
| Wenjing Chen | 3 | 0 | 0.34 |
| Heng Zhang | 4 | 0 | 0.34 |
| Tongzhou Kang | 5 | 0 | 0.34 |
| Han Yuan | 6 | 0 | 0.34 |
| Lei Ding | 7 | 142 | 26.77 |