Abstract: A system for predicting epileptic seizures includes sensors operable to record a wearer's brain activity. The sensors electronically communicate with a processor configured to receive and store output EEG oscillations and activities. A threshold electrical fluctuation level is identified as the level electrical activity experienced at the onset of a seizure event, and is then stored in the PDA memory as a predetermined threshold value. The processor analyzes the input EEG data logged for a recording period, and the logged data is broken into a number of data values across a series of individual set sampling periods. Convert collected data value readings for individual sampling periods as a non-linear measure value using fractal dimension, P&H and/or Lyapunov weighing. The calculated values for a predicted next time intervals extending the sampling period is projected forward and compared against the predetermined threshold value to indicate a likely seizure event.

Abstract: A system that is able to predict, model and monitor time lines of chaotic non-linier data or events such as commodity, stock and financial market performance indicators.

Abstract: A method for identifying peptides and proteins, starting from corresponding tandem spectrometry data. A structured representation is matched with a biological sequence database, and the best peptide match or matches within the database is determined. MS/MS data is interpreted and structured to allow full exploitation of the information contained in the data during matching of the structured data with a biological sequence database.