Applying a sociolinguistic approach to the study of neologisms, this paper discusses the actuation and diffusion of new words in Early Modern English (EModE; 1500–1700) and draws some parallels with word coining in the comparable but more recent period of Early Modern Finnish (EModF; 1810–1880). The success of this exercise ultimately depends on the data and tools available for ascertaining the status of neologisms in a broader synchronic and diachronic context. The use of historical dictionaries and digital databases shows that (...) many words that were earlier considered particular innovations in EModE are now up for re-evaluation. Determining their actual moments of coinage and entry into the language may be beyond historical lexicology and lexicography, but the new tools make it possible to better monitor their provenance and process of diffusion over time. (shrink)

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG (...) epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies. (shrink)