基于无监督学习的移动心电信号异常诊断研究
发布时间：2019-06-17 18:43
【摘要】：针对心电信号异常诊断,提出了一种基于无监督学习的移动心电信号异常诊断方法。该方法利用层次聚类将心电数据进行分类,同时结合特征量的优先级诊断分析法,有效避免了因移动心电信号的数据量过大而产生爆炸的时间复杂度和空间复杂度的问题。最后,通过心电信号实例验证了所提方法具有良好的可靠性和运行效率。
[Abstract]:Aiming at the abnormal diagnosis of ECG signal, an abnormal diagnosis method of mobile ECG signal based on unsupervised learning is proposed. In this method, ECG data are classified by hierarchical clustering, and the problem of explosion time complexity and space complexity caused by excessive amount of data of moving ECG signal is effectively avoided by combining the priority diagnosis analysis of feature quantity. Finally, an example of ECG signal shows that the proposed method has good reliability and operation efficiency.
【作者单位】： 东华大学计算机科学与技术学院;
【基金】：上海市自然基金(16ZR1401100)资助
【分类号】：R540.4;TP311.13
，
本文编号：2501182
[Abstract]:Aiming at the abnormal diagnosis of ECG signal, an abnormal diagnosis method of mobile ECG signal based on unsupervised learning is proposed. In this method, ECG data are classified by hierarchical clustering, and the problem of explosion time complexity and space complexity caused by excessive amount of data of moving ECG signal is effectively avoided by combining the priority diagnosis analysis of feature quantity. Finally, an example of ECG signal shows that the proposed method has good reliability and operation efficiency.
【作者单位】： 东华大学计算机科学与技术学院;
【基金】：上海市自然基金(16ZR1401100)资助
【分类号】：R540.4;TP311.13
，
本文编号：2501182
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