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首页> 外文期刊>Journal of ambient intelligence and humanized computing >Time series real time naive bayes electrocardiogram signal classification for efficient disease prediction using fuzzy rules
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Time series real time naive bayes electrocardiogram signal classification for efficient disease prediction using fuzzy rules

机译:时间序列实时朴素贝叶斯心电图信号分类以利用模糊规则进行高效疾病预测

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摘要

Towards the problem of ECG classification and disease prediction, various approaches are analyzed and discussed. However, the methods suffer to achieve higher performance in classification or disease prediction. To improve the performance, an efficient time series real time Naive Bayes ECG classification and disease prediction approach using fuzzy rule is presented in this paper. The method reads the ECG signals available and performs noise removal initially. From the graphs available, the features mentioned above are extracted and if there exist any incomplete or missing signal then the ECG sample has been removed from the data set. Once the preprocessing and feature extraction are done, then the features extracted. With the learned features, the method generates fuzzy rule for different disease class. The proposed algorithm computes posterior probability according to the mapping of different features of fuzzy rule. The classification or disease prediction is performed by measuring multi-feature signal similarity (MFSS). Estimated MFFS value has been used to measure the cardiac disease prone weight (CDPW) towards various classes available. According to the value of CDPW has been used to perform classification or disease prediction.
机译:针对心电图分类和疾病预测的问题,分析并讨论了各种方法。然而,该方法在分类或疾病预测中遭受更高的性能。为了提高性能,本文提出了一种有效的时间序列实时幼稚贝叶斯ECG分类和疾病预测方法。该方法读取可用的ECG信号并最初执行噪声删除。从可用的图形中,提取上述功能,如果存在任何不完整或缺失的信号,则从数据集中已删除ECG样本。一旦完成预处理和特征提取,就提取的特征就完成。通过学习功能,该方法为不同的疾病类生成模糊规则。该算法根据模糊规则的不同特征的映射来计算后概率。通过测量多特征信号相似度(MFSS)来执行分类或疾病预测。估计的MFF值已被用于测量可用的各种课程的心脏病易受重量(CDPW)。根据CDPW的价值已被用于进行分类或疾病预测。

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