Statistical approach for lightweight detection of anomalies in ECG.

In this paper the application of statistical approach for anomaly detection in a wearable ECG monitor has been considered. The method is based on the usage of the pre-defined expected behavior of the monitored biomedical signal and its on-line comparison with real-time measurements. Such comparison can be implemented based on the statistical model of ECG signal. To test the accuracy of the proposed method a ProSim 8 simulator was used to generate ECG signals. Our experiments showed that critical anomalies in ECG, such as Cardiac Failure, different types of Arrhythmia, and ST-segment deviations can be detected with high precision (96~100%), while false positive rate over typical NSR signal is low (<;4%). The method is scalable in terms of required performance, power consumption, and the precision of anomaly detection to be suitable for the implementation in a wearable platform.