Automatic Classification of Polysomnographic Respiration Signals

Within the project on biomedical signal analyses with artificial neural networks, recent research is focused on polysomnographic signal analyses. In polysomnographics (PSG) a large variety of physiological signals, obtained during a patients sleep, are analyzed and classified. This research focuses on the detection of obstructiveand central-apnea (absence of air flow through the nose), paradoxical respiration, normal respiration and artifacts in the noseflow , diaphragm and thorax signals. The signals are analyzed and characteristic features are extracted by different methods: FFT, correlation, filtering. The classification is implemented by the use of classical Bayes classification combined with relative modern neural network approaches. Multi-layer perceptrons (MLP) and Kohonen networks are used for probability density estimation of the features of the different classes. Multi-level classification and multi-level feature extraction were necessary. A dynamically, interactive, evolving..

Patient independent Spike-Wave Complex Detection in EEG Signals with an Artificial Neural Network

Spike-Wave Complexes in EEG signals may occur randomly in recordings of epileptic patients. Neurologists can recognize these complexes, which differ a lot from patient to patient. Automated Spike-Wave Complex detection systems have problems with these differences. Some of these systems are rule-based, others use features extracted from examples of Spike-Wave Complexes and normal EEG for detection. A detection system is proposed that uses examples of Spike-Wave Complexes as indicated by a neurologist. A neural network extracts a set of representative Spike-Wave Complexes, which are used for detection. The set of examples can be adapted to the neurologists to provide a user-adaptable detection system. Keywords--- Medical signal processing, pattern recognition, neural networks. I. Introduction The ElectroEncephaloGram (EEG) is a recording of the electrical activity of the brain. If the EEG is analyzed by frequency, four basic waves can be distinguished, alpha, beta, theta and delta wav..

Integrated Decision Support Tools for Disruption Management

During railway operations unexpected events can require railway operators and infrastructure managers to adjust their schedules. In this research we investigate the disruption management process. More specifically, we come up with an architecture and algorithmic framework which railway operators could use for decision support during disruptions. The use of this framework results in a fully feasible timetable, rolling stock plan, and crew schedule to deal with the disruption, while minimizing the number of delayed and/or (partially) cancelled trains. We demonstrate the effectiveness of our framework on a disruption case on the Dutch Railway network, which is introduced within the EU FP7 project ON-TIME.Transport & PlanningCivil Engineering and Geoscience