An Efficient Anomaly Detection in Quasi-Periodic Time Series Data—A Case Study with ECG
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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 the mother signal is the first step in the process. Finding deviations of subsequences of varied duration (due to quasi-periodicity) from mother 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.
KeywordsQuasi-periodic time series Anomaly detection Fundamental period Clustering
Part of this work was carried out under the cooperative research project program of the research institute of electrical communication, Tohoku University, and grant from Sendai Foundation of Applied Information Sciences, Sendai, Japan.
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