Patient-individual morphological anomaly detection in multi-lead electrocardiography data streams

Cardiac diseases like myocardial infarction, which possibly result in cardiac death, are still a relevant topic. To achieve recognitions in early stages, long term ECG monitoring devices are used. Such devices produce large amounts of data, either directly streamed or stored in databases. Manually analysing this data by experts is inefficient. Thus, automated preprocessing methods are needed to minimize the temporal effort dedicated to the inspection. The proposed method helps to identify morphological anomalies within the ECG data stream. It determines a set of meaningful time series features based on a Kolmogorov-Smirnov test (KST) and after that, applies the BICO online clustering algorithm. Thereby, the system learns the patient-individual PQRST-complex segment morphologies and after that, uses the learned models for detecting anomalies within the ECG data stream. For evaluation, real world patient data was used, which was previously tagged by electrophysiologists. As a result, the KST selected set of features was revealed to be especially suitable for analysing ECG data streams, resulting in average sensitivity rates of 98.82% and average specificity rates of 98.13%.