ABSTRACT
OBJECTIVE: High frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. METHODS: We detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. RESULTS: SOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. CONCLUSIONS: SOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SIGNIFICANCE: Visual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.
ABSTRACT
By early spring 2020, the COVID-19 pandemic caused mandatory campus closures of academic institutions nationwide, prompting the rapid transition to online instruction. While lectures and exams were more straightforwardly administered online using video-chatting software, many hands-on laboratory-based courses were forced to develop creative solutions. In response to online instructional requirements, instructors at the University of California Irvine developed an online electroencephalography (EEG) laboratory to simulate the laboratory experiment for students unable to perform the experiment on campus. The laboratory experiment was performed and video recorded by the instructional team under three different scenarios to provide students with multiple data sets acquired under various experimental conditions often enacted by students. Students were required to complete a pre-lab quiz, analyze the acquired EEG data offline, complete a post-lab quiz, and submit their laboratory report to communicate their findings prior to final exams. Student performances compared to prior student performances, and qualitative survey responses, were examined to assess the effectiveness of and response to the online laboratory format. Based on student feedback and lab report grades, the majority of students responded positively and demonstrated an understanding of the EEG experiment's learning outcomes. In summary, the online EEG laboratory enabled students to achieve the main learning objectives and become familiar with the laboratory experiment, indicating its success as an alternative laboratory experiment.
