Intelligent analysis and pattern recognition in cardiotocographic signals using a tightly coupled hybrid system
Autor: | Amparo Alonso-Betanzos, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas |
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Jazyk: | angličtina |
Předmět: |
Rule-based systems
Linguistics and Language Artificial neural network Computer science business.industry SIGNAL (programming language) Rule-based system Machine learning computer.software_genre Language and Linguistics Field (computer science) Antepartum foetal diagnosis Artificial Intelligence Antepartum foetal monitoring Hybrid system Pattern recognition (psychology) Computer-aided Artificial intelligence Intelligent hybrid systems business computer Neural networks |
Zdroj: | Artificial Intelligence. (1):1-27 |
ISSN: | 0004-3702 |
DOI: | 10.1016/S0004-3702(01)00163-1 |
Popis: | 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 performance—equivalent to expert performance—and thus confirm the suitability of AI techniques to applications in this field. |
Databáze: | OpenAIRE |
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