Privacy preserving anomaly detection based on local density estimation.

Anomaly detection has been widely researched in financial, biomedical and other areas. However, most existing algorithms have high time complexity. Another important problem is how to efficiently detect anomalies while protecting data privacy.

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.