基于知识的心电图诊断系统FLAPECAN

C. Held, J. Kurien
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引用次数: 2

摘要

FLAPECAN是一个基于知识的系统,旨在帮助解释心电图(ECGs)。诊断是基于被分析的心电图的简单特征。该系统可用于帮助由人类用户或由提供特征识别的自动分析系统生成诊断。该系统在知识表示系统FLX中采用基于模糊逻辑的推理实现。使用FLX,它提供了基于对象和规则的范例,产生了改进的组织,更明确地表示ECG领域知识,以及图形化的开发和用户环境。该系统对已知案例的反复应用为模糊知识的校准提供了结果。总的来说,FLAPECAN显示了良好的初步结果,这表明这种模糊推理的应用具有广阔的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FLAPECAN: a knowledge based system in electrocardiogram diagnosis
FLAPECAN is a knowledge-based system designed to aid in the interpretation of electrocardiograms (ECGs). Diagnoses are based upon simple features of the ECG being analyzed. The system may be used to aid in generating diagnoses either by a human user or by an automated analysis system where feature recognition is provided. The system was implemented with fuzzy-logic based inference in FLX, a knowledge representation system. The use of FLX, which provides object- and rule-based paradigms, yields improved organization, a more explicit representation of the ECG domain knowledge, and a graphical development and user environment. Repeated application of the system to known cases provided results for calibration of fuzzy knowledge. As a whole, FLAPECAN shows good initial results, suggesting that such an application of fuzzy reasoning has a promising future.<>
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