Motif Discovery and Anomaly Detection in an ECG Using Matrix Profile

Time Series Data mining is a popular field in data science to discover and extract useful information from the time series data. Time Series Motif discovery is one of the tasks in data mining to discover frequent patterns which are unknown previously. Motif discovery has gained a lot of attention since its advent in 2002. Many motif discovery techniques were introduced and applied in various domains like E-commerce, Weather Prediction, Seismology, etc. In this paper, we introduce a technique for anomaly detection and motif discovery in the ECG data using Matrix Profile which has been introduced recently in the literature. Anomaly detection in ECG helps to detect the abnormal heartbeats before the process of diagnosis and motif discovery helps to locate the highly similar beats in the ECG. Using Matrix Profile for the task of anomaly detection and motif discovery in our proposed technique provides our technique with properties that are inherited from Matrix Profile. Thus, the proposed technique in this paper has properties like exactness, simple and parameter-free, space-efficient, anytime, handle missing data, free from the curse of dimensionality.