AI Expert
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Hybrid architectures for intelligent systems
Hybrid architectures for intelligent systems
Hybrid Neural Network and Expert Systems
Hybrid Neural Network and Expert Systems
Intelligent Hybrid Systems
A Hybrid Intelligent System for the Preprocessing of Fetal Heart Rate Signals in Antenatal Testing
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
An Overview of Hybrid Neural Systems
Hybrid Neural Systems, revised papers from a workshop
Symbolic, Neural and Neuro-fuzzy Approaches to Pattern Recognition in Cardiotocograms
Advances in Computational Intelligence and Learning: Methods and Applications
Artificial Intelligence in Medicine
Automatic classification of intrapartal fetal heart-rate recordings: can it compete with experts?
ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
Decision Support Systems
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In obstetrics, cardiotocograph (CTG) and non-stress test readings are indispensable to antenatal monitoring and assessment. Difficulties in the interpretation of CTG records require methods for computer-assisted analysis. This article describes CAFE (Computer Aided Foetal Evaluator), an intelligent tightly coupled hybrid system developed to overcome the difficulties inherent in CTG analysis. It integrates algorithms (implemented via conventional programming techniques) with Artificial Intelligence (AI) paradigms (rule-based systems and artificial neural networks), in order to automate and perform all the phases involved in real time antenatal monitoring, from the analysis and interpretation of CTG signals to diagnosis. Its architecture, components and functional character will be described in detail. The validation of CAFE over 3450 minutes of signal time corresponding to 53 different patients in a real environment is discussed, and its performance with respect to a group of experts is evaluated. Most of the results obtained reflect acceptable levels of performanceequivalent to expert performanceand thus confirm the suitability of AI techniques to applications in this field.