Intelligent analysis and pattern recognition in cardiotocographic signals using a tightly coupled hybrid system

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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 performance—equivalent to expert performance—and thus confirm the suitability of AI techniques to applications in this field.

论文关键词:Intelligent hybrid systems,Neural networks,Rule-based systems,Antepartum foetal monitoring,Antepartum foetal diagnosis

论文评审过程:Received 27 July 1999, Revised 16 January 2001, Available online 30 October 2001.

论文官网地址:https://doi.org/10.1016/S0004-3702(01)00163-1