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PurposeExisting automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms.MethodsThe authors used video EEG recordings from 43 neonates undergoing monitoring for seizures as part of the NEOLEV2 clinical trial. The Persyst neonatal automated seizure detection algorithm ran in real time during study EEG acquisitions. Computer vision algorithms were applied to extract detailed accounts of artifactual movement of the neonate or people near the neonate though dense optical flow estimation.ResultsUsing the methods mentioned above, 197 periods of patting activity were identified and quantified, of which 45 generated false-positive automated seizure detection events. A binary patting detection algorithm was trained with a subset of 470 event videos. This supervised detection algorithm was applied to a testing subset of 187 event videos with 8 false-positive events, which resulted in a 24% reduction in false-positive automated seizure detections and a 50% reduction in false-positive events caused by neonatal care patting, while maintaining 11 of 12 true-positive seizure detection events.ConclusionsThis work presents a novel approach to improving automated seizure detection algorithms used during neonatal video EEG monitoring. This artifact detection mechanism can improve the ability of a seizure detector algorithm to distinguish between artifact and true seizure activity.

The primary purpose of this study was to examine the effectiveness of utilizing a combination of lecture and computer resources to train personnel to assume roles as hydraulic system technicians and specialists in the fluid power industry. This study compared computer simulated laboratory instruction to traditional hands-on laboratory instruction, in terms of effectiveness. This study used a within-subjects repeated measures design to determine the relationship between two methods of teaching fluid power laboratory experiences and student achievement on a cognitive written instrument, as well as a performance (psychomotor) instrument. The findings of the study include: (1) There was not a statistically significant difference between the performance of the two treatment groups on the psychomotor instrument after the completion of the first two laboratory assignments; (2) The subjects classified as field-independent learners scored statistically significantly higher on the mid-lab psychomotor performance test than did the field-dependent learners; (3) There was not a statistically significant difference in student performance on the post-lab psychomotor performance evaluation as a result of the sequencing of the treatment types between the two groups; (4) The subjects classified as field-independent learners scored statistically significantly higher on the end-of-lab psychomotor performance test than did the field-dependent learners; and (5) The difference between the mid- and post- psychomotor test scores within each group as a result of the two different methods of treatment sequencing was not statistically significant. (Contains 1 table.)

Continuous EEG monitoring (CEEG) is a powerful tool for evaluating cerebral function in obtunded and comatose critically ill patients. The ongoing analysis of CEEG data is a major task because of the volume of data generated during monitoring and the need for near real-time interpretation of a patient's EEG patterns. Advances in digital EEG data acquisition, computer processing, data transmission, and data display have made CEEG monitoring in the intensive care unit technically feasible. A variety of quantitative EEG tools such as Fourier analysis and amplitude-integrated EEG, and other methods of data analysis such as computerized seizure detection, increasingly allow for focused review of EEG epochs of potential interest. These tools reduce the tremendous time burdens that accompany analysis of the complete CEEG data stream, and allow bedside personnel and nonexpert staff to potentially recognize significant EEG changes in a timely fashion. This article uses literature review and clinical case examples to illustrate techniques for the display and analysis of intensive care unit CEEG recordings. Areas requiring further research and development are discussed.