Massive amounts of electronic health records, including data on vital signs and electrocardiograms (ECGs), are now accessible due to the big data revolution. These signals are now more easily obtained and are frequently captured as a time series of observations. There is a particular need to provide innovative methods that enable efficient monitoring of these signals and prompt anomaly detection given the proliferation of smart devices with ECG capabilities. However, anomaly identification is still a very difficult task because the majority of created data is not yet categorized.