  ECGs objectively reflects the working conditions of the hearts as these
signals contain vast physiological and pathological information. In this work,
in order to improve the efficiency and accuracy of "best so far" time series
analysis-based ECG anomaly detection methods, a novel method, comprising a
modified dynamic time warping (MDTW) and an innovative average non-self match
distance (ANSD) measure, is proposed for ECG anomaly detection. To evaluate the
performance of the proposed method, the proposed method is applied to real ECG
data selected from the MIT-BIH heartbeat database. To provide a reference for
comparison, two existing anomaly detection methods, namely, brute force discord
discovery (BFDD) and adaptive window discord discovery (AWDD), are also applied
to the same data. The experimental results show that our proposed method
outperforms BFDD and AWD.
