Upgrading undergraduate biomedical engineering laboratory training.

This paper presents the upgrading of biomedical engineering laboratory training at the Department of Medical Instrumentation Technology of the Technological Educational Institution of Athens (TEI-A), taking place in the framework of the "Upgrading of Undergraduate Curricula of TEI-A" project. The educational material of selected specialized laboratory sectors is totally renewed, and new sectors are introduced, so that student-centered learning is promoted utilizing advanced computer-enhanced educational environments. The current implementation status is presented for the laboratories dealing with biosignal acquisition, medical data digital processing and, more extensively, computer networks applications in medicine, where a training application simulating a Radiology Department computer network was developed.

Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study.

An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.

Sleep spindle incidence dynamics: a pilot study based on a Markovian analysis.

Results are reported, based on 5 healthy subjects, concerning patterns in the dynamics of the sequential arrangement of spindles in human stage 2 sleep. Specifically, the conditional probability of incidence of successive spindle lengths and successive inter-spindle intervals is investigated. The results show that successive spindle lengths may be statistically independent.