PCA-based multivariate anomaly detection in mobile healthcare applications
PCA-based multivariate anomaly detection in mobile healthcare applications
复制标题
移动医疗应用中基于 PCA 的多变量异常检测
DOI：
10.1109/distra.2017.8167682
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Jmaiel
中科院分区：
文献类型：
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作者：
Lamia Ben Amor;Imene Lahyani;M. Jmaiel
真实的实时移动的健康应用高度依赖于传感器读数来提供高质量的健康服务。然而，由于内部和外部因素，实时传感器读数可能不准确并导致异常生理测量。因此，异常读数对这种应用的可靠性具有显著影响，并因此影响患者的生活。本文通过提出一种用于在线检测异常医学测量的鲁棒方法来解决以下问题。所提出的方法是基于强大的主成分分析（PCA），分析收集的生理测量传感器和检测的基础上，在运行时的预测误差平方的多变量异常的发生。我们将我们提出的方法应用于真实的医疗数据集。我们的仿真结果证明了我们的方法在实现良好的召回率与低误报率的有效性。我们的方法降低了时间和空间的复杂性，使其有用和有效的真实的时间设置。
Real time mobile Health applications highly depend on sensor readings to provide high-quality health services. However, real-time sensor readings may be inaccurate and cause abnormal physiological measurements due to internal and external factors. Thus, abnormal readings have a significant impact on the reliability of such applications and consequently affect the patient's life. This paper addresses the following issue by proposing a robust approach for online detection of abnormal medical measurements. The proposed approach is based on robust Principal Component Analysis (PCA) to analyze collected physiological measurements from sensors and detect the occurrence of multivariate anomalies based on squared prediction error at runtime. We apply our proposed approach on real medical dataset. Our simulation results prove the effectiveness of our approach in achieving good recall with a low false alarm rate. The reduced time and space complexity of our approach make it useful and efficient for real time settings.