心率趋势和伪影的模糊分类

[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems Pub Date : 1992-06-14 DOI:10.1109/CBMS.1992.245009

Dean F. Sittig, K. Cheung, L. Berman

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引用次数: 5

摘要

模糊集理论使得通过用户定义的隶属函数将不精确的数据、概念和事件映射到模糊集成为可能。作者描述了一种方法:(1)稳健地估计任意数量数据点的平均值和斜率，(2)开发一组模糊隶属函数来分类心率趋势的各种属性，以及(3)找到适合特定模糊隶属函数的最长连续心率数据序列。初步结果表明，模糊集理论在开发一种临床鲁棒方法来分类心率数据、趋势和伪像方面具有重大潜力。

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Fuzzy classification of heart rate trends and artifacts

Fuzzy set theory makes it possible to map inexact data, concepts, and events to fuzzy sets via user-defined membership functions. The authors describe a method for (1) robustly estimating the mean and slope of an arbitrary number of data points, (2) developing a set of fuzzy membership functions to classify various properties of heart rate trends, and (3) finding the longest consecutive sequence of heart rate data that fit a particular fuzzy membership function. Preliminary results indicate that fuzzy set theory has significant potential in the development of a clinically robust method for classifying heart rate data, trends, and artifacts.<>

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[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems

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