Characterization of high frequency oscillations in a small Hall-type thruster

1999 ◽  
Vol 6 (1) ◽  
pp. 343-349 ◽  
Author(s):  
G. Guerrini ◽  
C. Michaut
2018 ◽  
Vol 28 (07) ◽  
pp. 1850001 ◽  
Author(s):  
Lucia Rita Quitadamo ◽  
Roberto Mai ◽  
Francesca Gozzo ◽  
Veronica Pelliccia ◽  
Francesco Cardinale ◽  
...  

Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time–frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80–250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.


2020 ◽  
Author(s):  
Steven Tobochnik ◽  
Lisa M. Bateman ◽  
Cigdem I. Akman ◽  
Deepti Anbarasan ◽  
Carl W. Bazil ◽  
...  

Objective: Characterization of progressive multi-site seizure recruitment using high frequency oscillations. Methods: Ictal and interictal high frequency oscillations were identified in a series of 13 patients with 72 seizures recorded by stereotactic depth electrodes, using previously validated methods. Channels with ictal high frequency oscillations were assigned to distinct spatial clusters, and seizure hubs were identified by stereotypically recruited non-overlapping clusters. Clusters were correlated with asynchronous seizure terminations to provide supportive evidence for independent seizure activity at these sites. The spatial overlap of ictal and interictal high frequency oscillations were compared. Results: Ictal high frequency oscillations were detected in 71% of seizures and 10% of implanted contacts, enabling tracking of contiguous and noncontiguous seizure recruitment. Multiple seizure hubs were identified in 54% of cases, including 43% of patients thought preoperatively to have unifocal epilepsy. Noncontiguous recruitment was associated with asynchronous seizure termination (Odds Ratio=10, 95% CI 2.9-41, p<0.001). Interictal high frequency oscillations demonstrated greater spatial overlap with ictal high frequency oscillations in cases with single seizure hubs than in those with multiple hubs (100% vs 66% per patient, p=0.03). Significance: Analysis of ictal high frequency oscillations can serve as a useful adjunctive technique to distinguish contiguous seizure spread from propagation to remote seizure sites. This study demonstrated that multiple seizure hubs were commonly identified by spatial clustering of ictal high frequency oscillations, including in cases that were considered unifocal. The distinction between initially activated and delayed seizure hubs was not evident based on interictal high frequency analysis, but may provide important prognostic information.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 45531-45543 ◽  
Author(s):  
Dakun Lai ◽  
Xinyue Zhang ◽  
Wenjing Chen ◽  
Heng Zhang ◽  
Tongzhou Kang ◽  
...  

Brain ◽  
2009 ◽  
Vol 132 (11) ◽  
pp. 3047-3059 ◽  
Author(s):  
C. A. Schevon ◽  
A. J. Trevelyan ◽  
C. E. Schroeder ◽  
R. R. Goodman ◽  
G. McKhann ◽  
...  

2010 ◽  
Vol 31 (3) ◽  
pp. 353-359
Author(s):  
Xiaoyan CHAI ◽  
Shuyong SHANG ◽  
Gaihuan LIU ◽  
Xumei TAO ◽  
Xiang LI ◽  
...  

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