A syntactic analysis method for sinusoidal tracking eye movements
Computers and Biomedical Research
A hybrid expert system for avalanche forecasting
Proceedings of the international conference on Information and communications technologies in tourism
Knowledge processing in neural architecture
Proceeding of an international workshop on VLSI for neural networks and artificial intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Intelligent Hybrid Systems
Evaluation of Automatic and Manual Knowledge Acquisition for Cerebrospinal Fluid (CSF) Diagnosis
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Syntactic recognition of ECG signals by attributed finite automata
Pattern Recognition
Structuring ordered nominal data for event sequence discovery
Proceedings of the international conference on Multimedia
A review on time series data mining
Engineering Applications of Artificial Intelligence
A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders
Artificial Intelligence in Medicine
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In this paper we present a new method for temporal knowledge conversion, called TCon. The main aim of our approach is to perform a transition, i.e. conversion, of temporal complex patterns in multivariate time series to a linguistic, for human beings understandable description of the patterns. The main idea for the detection of those complex patterns lies in breaking down a highly structured and complex problem into several subtasks. Therefore, several abstraction levels have been introduced where at each level temporal complex patterns are detected successively using exploratory methods, namely unsupervised neural networks together with special visualization techniques. At each level, temporal grammatical rules are extracted. The method TCon was applied to a problem from medicine, sleep apnea. It is a hard problem since quite different patterns may occur, even for the same patient, as well as the duration of each pattern may differ strongly. Altogether, all patterns have been detected and a meaningful description of the patterns was generated. Even some kind of "new" knowledge was found.