Anomalies Detection Approach in Electrocardiogram Analysis Using Linguistic Modeling

Abstract

This work proposes a new approach for identifying heart anomalies on electrocardiograms data using linguistic modeling. The process of identifying anomalies in the proposed approach consists of the following subtasks: the subtask of interval splitting, the subtask of linguistics, the subtask of anomalies searching. The approach includes: the creation of a linguistic pattern database, represent ECG as well as linguistic chain, use linguistic pattern database to search for linguistic chain parts based on abnormal patterns. Linguistic model is suggested for the creation of an anomalies database for further detection in a cardiogram, reproduced in the form of a linguistic chain. Storing ECG as a linguistic model facilitates is easy for data storing and data search in patient history. Linguistic pattern database has filling stages: signal conversion in digital time series, interval splitting, matching interval with an alphabet symbol, creation of alphabetic symbol time series. Anomalies search based on seeking abnormal linguistic patterns in ECG linguistic chains.