Of snakes and flowers: Does preferential detection of pictures of fear-relevant animals in visual search reflect on fear-relevance?

Previous research in visual search indicates that animal fear-relevant deviants, snakes/spiders, are found faster among non fear-relevant backgrounds, flowers/mushrooms, than vice versa. Moreover, deviant absence was indicated faster among snakes/spiders and detection time for flower/mushroom deviants, but not for snake/spider deviants, increased in larger arrays. The current research indicates that the latter 2 results do not reflect on fear-relevance, but are found only with flower/mushroom controls. These findings may reflect on factors such as background homogeneity, deviant homogeneity, or background-deviant similarity. The current research removes contradictions between previous studies that used animal and social fear-relevant stimuli and indicates that apparent search advantages for fear-relevant deviants seem likely to reflect on delayed attentional disengagement from fear-relevance on control trials.

Seizure detection algorithm for neonates based on wave-sequence analysis

Objective: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. Methods: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. Results: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30 s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. Conclusions: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. Significance: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring. Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.