Anomaly detection in ECG based on trend symbolic aggregate approximation.

ECG anomaly detection is a necessary approach to detect disease Electrocardiography( ECG) signals before the detail diagnosis process in medical field to gauge the health of the human heart. Nowadays, there are many anomaly detection methods for ECG detection including supervised learning and unsupervised learning. For supervised learning, it requires the knowledge of expert and different types of Arrhythmia data for training. However, since the anomalies are less and unknown in many cases which are di cult to distinguish and be labeled, unsupervised methods are more suitable to detect the ECG anomalies. Furthermore, the existing unsupervised learning studies do not take ECG shape into account where different diseases have different shapes. In this paper, a novel simple trend aggregate approximation method is proposed, the relative binary trend representation are used to record the shape feature in original time series and to detect the anomaly heart signals by similarity comparison. We use the ECG dataset in UCR Time Series Classification Archive to obtain ECG time series data and the experiment results are assessed by means of sensitivity, specificity, false alarm rate measures which is robust and promising with high accuracy.