An Efficient Anomaly Detection in Quasi-Periodic Time Series Data - A Case Study with ECG
An Efficient Anomaly Detection in Quasi-Periodic Time Series Data - A Case Study with ECG
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准周期时间序列数据中的有效异常检测 - ECG 案例研究
DOI：
10.1007/978-3-319-96944-2_10
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Tetsuo Kinoshita
中科院分区：
文献类型：
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作者：
Goutam Chakraborty;Takuya Kamiyama;Hideyuki Takahashi;Tetsuo Kinoshita
从时间序列中进行异常检测是发现或预测系统故障发展的一个重要问题。根据数据源的不同，它可以是非周期、准周期和周期的。对非周期数据进行建模以检测异常是困难的。纯周期数据在自然界中很少出现。在准周期时间序列信号中发现异常，例如心电、心率(脉搏)数据等生物信号，是非常重要的。但是，由于需要适当的窗口大小选择和窗口大小持续时间的每对子序列的比较，所以分析在计算上是复杂的。本文提出了一种有效的准周期时间序列异常检测算法。我们引入了“母信号”的概念，它是正规子序列的平均。创建母亲信号是这一过程的第一步。第二步是找出不同持续时间(由于准周期)的子序列与母信号的偏差。当这个距离超过一个门槛时，它被宣布为不和谐。该算法足够轻巧，可以在手机等计算能力较弱的平台上实时工作。对心电信号进行了实验，评价了算法的性能。与已有工作相比，该方法具有更高的计算效率，并能以更高的识别率识别不一致。
Anomaly detection from a time series is an important problem with applications to find or predict the development of a fault in a system. Depending on the source of the data, it could be nonperiodic, quasi-periodic, and periodic. Modeling an aperiodic data to detect anomaly is difficult. A pure periodic data seldom happens in nature. Finding anomaly in quasi-periodic time series signals, for example, bio-signals like ECG, heart rate (pulse) data, are important. But, the analysis is computationally complex because of the need for proper window size selection and comparison of every pair of subsequences of window-size duration. In this paper, we proposed an efficient algorithm for anomaly detection of quasi-periodic time series data. We introduced a new concept “mother signal”, which is the average of normal subsequences. Creation of themother signalis the first step in the process. Finding deviations of subsequences of varied duration (due to quasi-periodicity) frommother signal, is the second step. When this distance crosses a threshold, it is declared as a discord. The algorithm is light enough to work in real-time on computationally weak platforms like a mobile phone. Experiments were done with ECG signals to evaluate the performance. It is shown to be computationally more efficient compared to existing works, and could identify discords with higher rate.