Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification.

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial.

Integration of DASH and ICS 3000 devices with Hospital Information System and REPACE central registry.

Paper describes the system that is developed by the authors and which allows interconnection of vital signs monitor DASH, implant control system ICS 3000, HIS (Hospital Information System) used at Městská nemocnice Ostrava and REPACE the central registry of pacemaker implantations. This connection allows users to effectively create, store and visualize operation reports with patient's data. The new system is being developed using C# programming language within .

Use of mobile phones as intelligent sensors for sound input analysis and sleep state detection.

Sleep is not just a passive process, but rather a highly dynamic process that is terminated by waking up. Throughout the night a specific number of sleep stages that are repeatedly changing in various periods of time take place. These specific time intervals and specific sleep stages are very important for the wake up event.